{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "# Importing The data and preprocessing" ], "metadata": { "id": "o01mOtABchVv" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "7vSnAq8auv2a" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n" ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OS_yd77xmvau", "outputId": "5829d46d-c92c-48be-e610-56c311bb9b84" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "data=pd.read_excel('/content/drive/MyDrive/Dataset/Dataset.xlsx')\n", "data.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "5oWpS54uh6rS", "outputId": "c3b9eff7-2587-43f7-afdf-ca2cf1d9dc5e" }, "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Airline Date_of_Journey Source Destination Route \\\n", "0 IndiGo 24/03/2019 Banglore New Delhi BLR → DEL \n", "1 Air India 1/05/2019 Kolkata Banglore CCU → IXR → BBI → BLR \n", "2 Jet Airways 9/06/2019 Delhi Cochin DEL → LKO → BOM → COK \n", "3 IndiGo 12/05/2019 Kolkata Banglore CCU → NAG → BLR \n", "4 IndiGo 01/03/2019 Banglore New Delhi BLR → NAG → DEL \n", "\n", " Dep_Time Arrival_Time Duration Total_Stops Additional_Info Price \n", "0 22:20 01:10 22 Mar 2h 50m non-stop No info 3897 \n", "1 05:50 13:15 7h 25m 2 stops No info 7662 \n", "2 09:25 04:25 10 Jun 19h 2 stops No info 13882 \n", "3 18:05 23:30 5h 25m 1 stop No info 6218 \n", "4 16:50 21:35 4h 45m 1 stop No info 13302 " ], "text/html": [ "\n", "\n", "
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AirlineDate_of_JourneySourceDestinationRouteDep_TimeArrival_TimeDurationTotal_StopsAdditional_InfoPrice
0IndiGo24/03/2019BangloreNew DelhiBLR → DEL22:2001:10 22 Mar2h 50mnon-stopNo info3897
1Air India1/05/2019KolkataBangloreCCU → IXR → BBI → BLR05:5013:157h 25m2 stopsNo info7662
2Jet Airways9/06/2019DelhiCochinDEL → LKO → BOM → COK09:2504:25 10 Jun19h2 stopsNo info13882
3IndiGo12/05/2019KolkataBangloreCCU → NAG → BLR18:0523:305h 25m1 stopNo info6218
4IndiGo01/03/2019BangloreNew DelhiBLR → NAG → DEL16:5021:354h 45m1 stopNo info13302
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\n" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "source": [ "data.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AxYcGBitiOhK", "outputId": "3f1ec570-385a-47f9-ec71-0fae8e83d462" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 10683 entries, 0 to 10682\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Airline 10683 non-null object\n", " 1 Date_of_Journey 10683 non-null object\n", " 2 Source 10683 non-null object\n", " 3 Destination 10683 non-null object\n", " 4 Route 10682 non-null object\n", " 5 Dep_Time 10683 non-null object\n", " 6 Arrival_Time 10683 non-null object\n", " 7 Duration 10683 non-null object\n", " 8 Total_Stops 10682 non-null object\n", " 9 Additional_Info 10683 non-null object\n", " 10 Price 10683 non-null int64 \n", "dtypes: int64(1), object(10)\n", "memory usage: 918.2+ KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "There are 9 features and 1 target variable" ], "metadata": { "id": "LBzdTbn9iWLS" } }, { "cell_type": "code", "source": [ "#checking for null values if any\n", "data.isnull().sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uo4I7LuYicK3", "outputId": "6652d6f0-a5cb-4095-e922-6437cd380d3c" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Airline 0\n", "Date_of_Journey 0\n", "Source 0\n", "Destination 0\n", "Route 1\n", "Dep_Time 0\n", "Arrival_Time 0\n", "Duration 0\n", "Total_Stops 1\n", "Additional_Info 0\n", "Price 0\n", "dtype: int64" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "These empty values are quite low so dropping them" ], "metadata": { "id": "DzvpZwSPippy" } }, { "cell_type": "code", "source": [ "data.dropna(inplace = True)" ], "metadata": { "id": "qtlxM9lQim3f" }, "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "source": [ "Dropping duplicates rows if any" ], "metadata": { "id": "Mp_kwLGijKG1" } }, { "cell_type": "code", "source": [ "data.drop_duplicates(inplace=True)" ], "metadata": { "id": "ngYaw3Ghi4e_" }, "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "source": [ "Visualization" ], "metadata": { "id": "3qbV8qRBjcmP" } }, { "cell_type": "markdown", "source": [ "Converting the date and times" ], "metadata": { "id": "AVSeONgRu87n" } }, { "cell_type": "code", "source": [ "data[\"Journey_day\"] = pd.to_datetime(data.Date_of_Journey, format=\"%d/%m/%Y\").dt.day" ], "metadata": { "id": "ppBzqcpaku-f" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "data[\"Journey_month\"] = pd.to_datetime(data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month" ], "metadata": { "id": "Vky6Dh1Rup7c" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "data.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "NUk0waDwu4K0", "outputId": "9c31c825-badf-4444-8491-a5cd0b8b0526" }, "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Airline Date_of_Journey Source Destination Route \\\n", "0 IndiGo 24/03/2019 Banglore New Delhi BLR → DEL \n", "1 Air India 1/05/2019 Kolkata Banglore CCU → IXR → BBI → BLR \n", "2 Jet Airways 9/06/2019 Delhi Cochin DEL → LKO → BOM → COK \n", "3 IndiGo 12/05/2019 Kolkata Banglore CCU → NAG → BLR \n", "4 IndiGo 01/03/2019 Banglore New Delhi BLR → NAG → DEL \n", "\n", " Dep_Time Arrival_Time Duration Total_Stops Additional_Info Price \\\n", "0 22:20 01:10 22 Mar 2h 50m non-stop No info 3897 \n", "1 05:50 13:15 7h 25m 2 stops No info 7662 \n", "2 09:25 04:25 10 Jun 19h 2 stops No info 13882 \n", "3 18:05 23:30 5h 25m 1 stop No info 6218 \n", "4 16:50 21:35 4h 45m 1 stop No info 13302 \n", "\n", " Journey_day Journey_month \n", "0 24 3 \n", "1 1 5 \n", "2 9 6 \n", "3 12 5 \n", "4 1 3 " ], "text/html": [ "\n", "\n", "
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AirlineDate_of_JourneySourceDestinationRouteDep_TimeArrival_TimeDurationTotal_StopsAdditional_InfoPriceJourney_dayJourney_month
0IndiGo24/03/2019BangloreNew DelhiBLR → DEL22:2001:10 22 Mar2h 50mnon-stopNo info3897243
1Air India1/05/2019KolkataBangloreCCU → IXR → BBI → BLR05:5013:157h 25m2 stopsNo info766215
2Jet Airways9/06/2019DelhiCochinDEL → LKO → BOM → COK09:2504:25 10 Jun19h2 stopsNo info1388296
3IndiGo12/05/2019KolkataBangloreCCU → NAG → BLR18:0523:305h 25m1 stopNo info6218125
4IndiGo01/03/2019BangloreNew DelhiBLR → NAG → DEL16:5021:354h 45m1 stopNo info1330213
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\n" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "#DAte 0f journey is now of no use so dropping it\n", "data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)" ], "metadata": { "id": "oOguMzM-vB7j" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "#converting the dep time into hours and minutes\n", "data['Dep_hour']=pd.to_datetime(data[\"Dep_Time\"]).dt.hour\n", "#extracting the minutes\n", "data['Dep_min']=pd.to_datetime(data[\"Dep_Time\"]).dt.minute\n", "\n", "#Now we can drop the date time and it is not of use\n", "data.drop([\"Dep_Time\"], axis = 1, inplace = True)" ], "metadata": { "id": "iOOO2UravW6A" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "data.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "cG2GyHzjwodK", "outputId": "301bb964-ef22-447b-9fca-05309ecd46a2" }, "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Airline Source Destination Route Arrival_Time \\\n", "0 IndiGo Banglore New Delhi BLR → DEL 01:10 22 Mar \n", "1 Air India Kolkata Banglore CCU → IXR → BBI → BLR 13:15 \n", "2 Jet Airways Delhi Cochin DEL → LKO → BOM → COK 04:25 10 Jun \n", "3 IndiGo Kolkata Banglore CCU → NAG → BLR 23:30 \n", "4 IndiGo Banglore New Delhi BLR → NAG → DEL 21:35 \n", "\n", " Duration Total_Stops Additional_Info Price Journey_day Journey_month \\\n", "0 2h 50m non-stop No info 3897 24 3 \n", "1 7h 25m 2 stops No info 7662 1 5 \n", "2 19h 2 stops No info 13882 9 6 \n", "3 5h 25m 1 stop No info 6218 12 5 \n", "4 4h 45m 1 stop No info 13302 1 3 \n", "\n", " Dep_hour Dep_min \n", "0 22 20 \n", "1 5 50 \n", "2 9 25 \n", "3 18 5 \n", "4 16 50 " ], "text/html": [ "\n", "\n", "
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AirlineSourceDestinationRouteArrival_TimeDurationTotal_StopsAdditional_InfoPriceJourney_dayJourney_monthDep_hourDep_min
0IndiGoBangloreNew DelhiBLR → DEL01:10 22 Mar2h 50mnon-stopNo info38972432220
1Air IndiaKolkataBangloreCCU → IXR → BBI → BLR13:157h 25m2 stopsNo info766215550
2Jet AirwaysDelhiCochinDEL → LKO → BOM → COK04:25 10 Jun19h2 stopsNo info1388296925
3IndiGoKolkataBangloreCCU → NAG → BLR23:305h 25m1 stopNo info6218125185
4IndiGoBangloreNew DelhiBLR → NAG → DEL21:354h 45m1 stopNo info13302131650
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\n" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "code", "source": [ "# Arrival time is when the plane pulls up to the gate.\n", "# Similar to Date_of_Journey we can extract values from Arrival_Time\n", "\n", "# Extracting Hours\n", "data[\"Arrival_hour\"] = pd.to_datetime(data.Arrival_Time).dt.hour\n", "\n", "# Extracting Minutes\n", "data[\"Arrival_min\"] = pd.to_datetime(data.Arrival_Time).dt.minute\n", "\n", "# Now we can drop Arrival_Time as it is of no use\n", "data.drop([\"Arrival_Time\"], axis = 1, inplace = True)" ], "metadata": { "id": "RCErZ0iywqnc" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "data.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "Utiri2DpxN7n", "outputId": "f5c101a6-99a0-403b-e058-87d05be900fd" }, "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Airline Source Destination Route Duration \\\n", "0 IndiGo Banglore New Delhi BLR → DEL 2h 50m \n", "1 Air India Kolkata Banglore CCU → IXR → BBI → BLR 7h 25m \n", "2 Jet Airways Delhi Cochin DEL → LKO → BOM → COK 19h \n", "3 IndiGo Kolkata Banglore CCU → NAG → BLR 5h 25m \n", "4 IndiGo Banglore New Delhi BLR → NAG → DEL 4h 45m \n", "\n", " Total_Stops Additional_Info Price Journey_day Journey_month Dep_hour \\\n", "0 non-stop No info 3897 24 3 22 \n", "1 2 stops No info 7662 1 5 5 \n", "2 2 stops No info 13882 9 6 9 \n", "3 1 stop No info 6218 12 5 18 \n", "4 1 stop No info 13302 1 3 16 \n", "\n", " Dep_min Arrival_hour Arrival_min \n", "0 20 1 10 \n", "1 50 13 15 \n", "2 25 4 25 \n", "3 5 23 30 \n", "4 50 21 35 " ], "text/html": [ "\n", "\n", "
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AirlineSourceDestinationRouteDurationTotal_StopsAdditional_InfoPriceJourney_dayJourney_monthDep_hourDep_minArrival_hourArrival_min
0IndiGoBangloreNew DelhiBLR → DEL2h 50mnon-stopNo info38972432220110
1Air IndiaKolkataBangloreCCU → IXR → BBI → BLR7h 25m2 stopsNo info7662155501315
2Jet AirwaysDelhiCochinDEL → LKO → BOM → COK19h2 stopsNo info1388296925425
3IndiGoKolkataBangloreCCU → NAG → BLR5h 25m1 stopNo info62181251852330
4IndiGoBangloreNew DelhiBLR → NAG → DEL4h 45m1 stopNo info133021316502135
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\n" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "code", "source": [ "# Assigning and converting Duration column into list\n", "duration = list(data[\"Duration\"])" ], "metadata": { "id": "v68C1uRvxQVy" }, "execution_count": 16, "outputs": [] }, { "cell_type": "code", "source": [ "#taking the durations\n", "for i in range(len(duration)):\n", " if len(duration[i].split()) != 2:\n", " if \"h\" in duration[i]:\n", " duration[i] = duration[i].strip() + \" 0m\"\n", " else:\n", " duration[i] = \"0h \" + duration[i]\n", "\n", "duration_hours = []\n", "duration_mins = []\n", "for i in range(len(duration)):\n", " duration_hours.append(int(duration[i].split(sep = \"h\")[0]))\n", " duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))" ], "metadata": { "id": "IlREQHKcxpX1" }, "execution_count": 17, "outputs": [] }, { "cell_type": "code", "source": [ "data[\"Duration_hours\"] = duration_hours\n", "data[\"Duration_mins\"] = duration_mins" ], "metadata": { "id": "w_SyRaE-x6js" }, "execution_count": 18, "outputs": [] }, { "cell_type": "code", "source": [ "data.drop([\"Duration\"], axis = 1, inplace = True)" ], "metadata": { "id": "7bm_D-L4yARV" }, "execution_count": 19, "outputs": [] }, { "cell_type": "code", "source": [ "data.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "id": "552zYEn4yVTx", "outputId": "95bca8bd-efa9-475f-c368-ea5c85b176be" }, "execution_count": 20, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Airline Source Destination Route Total_Stops \\\n", "0 IndiGo Banglore New Delhi BLR → DEL non-stop \n", "1 Air India Kolkata Banglore CCU → IXR → BBI → BLR 2 stops \n", "2 Jet Airways Delhi Cochin DEL → LKO → BOM → COK 2 stops \n", "3 IndiGo Kolkata Banglore CCU → NAG → BLR 1 stop \n", "4 IndiGo Banglore New Delhi BLR → NAG → DEL 1 stop \n", "\n", " Additional_Info Price Journey_day Journey_month Dep_hour Dep_min \\\n", "0 No info 3897 24 3 22 20 \n", "1 No info 7662 1 5 5 50 \n", "2 No info 13882 9 6 9 25 \n", "3 No info 6218 12 5 18 5 \n", "4 No info 13302 1 3 16 50 \n", "\n", " Arrival_hour Arrival_min Duration_hours Duration_mins \n", "0 1 10 2 50 \n", "1 13 15 7 25 \n", "2 4 25 19 0 \n", "3 23 30 5 25 \n", "4 21 35 4 45 " ], "text/html": [ "\n", "\n", "
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AirlineSourceDestinationRouteTotal_StopsAdditional_InfoPriceJourney_dayJourney_monthDep_hourDep_minArrival_hourArrival_minDuration_hoursDuration_mins
0IndiGoBangloreNew DelhiBLR → DELnon-stopNo info38972432220110250
1Air IndiaKolkataBangloreCCU → IXR → BBI → BLR2 stopsNo info7662155501315725
2Jet AirwaysDelhiCochinDEL → LKO → BOM → COK2 stopsNo info1388296925425190
3IndiGoKolkataBangloreCCU → NAG → BLR1 stopNo info62181251852330525
4IndiGoBangloreNew DelhiBLR → NAG → DEL1 stopNo info133021316502135445
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\n", "
\n" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "markdown", "source": [ "# Handling the categorical data" ], "metadata": { "id": "1792aAnuyjTW" } }, { "cell_type": "code", "source": [ "data[[\"Source\"]].value_counts()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RYWKDHc0zZSm", "outputId": "20e148f5-b0f6-4476-c540-f3d382278351" }, "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Source \n", "Delhi 4345\n", "Kolkata 2860\n", "Banglore 2179\n", "Mumbai 697\n", "Chennai 381\n", "dtype: int64" ] }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "source": [ "# Plotting Violin plot for Price vs Source\n", "sns.catplot(y = \"Price\", x = \"Source\", data = data.sort_values(\"Price\", ascending = False), kind=\"violin\", height = 4, aspect = 3)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 351 }, "id": "c6NzHmjSjgQx", "outputId": "3d7107ec-3a20-4ef2-e357-83687edee6a5" }, "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Plotting Price vs Airline plot\n", "sns.catplot(y = \"Price\", x = \"Airline\", data = data.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 8, aspect = 3)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 353 }, "id": "zeb_NtFTjI8q", "outputId": "00f946ee-2834-49a7-d9ea-fa59a3790f72" }, "execution_count": 23, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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tEqsAIMDZg4MUGszuGgBqs/sfHKeQkOAix+XkODX73mV+jggA4A8RERHq2rWrtm/f7hnWrVs3qvagVrDZbJoxY4aSkpI0c+ZM2WxcF6P2cLlcWrhwoU6ePKmFCxcqJSWFbQAAAAAATGI1OwAAAAAAJQsJCZbdXvSruIQrAED1l5mZqd27d3sN2717tzIzM02KCPCv6OhorVq1imo9qHXWrFmjn376SZJ0+PBhrV271uSIAAAAAKD2IrEKAAAAAAAgAKWkpMjlcnkNczqdWrx4sUkRAf4XFBRkdgiAX2VmZmrZMu+KtKmpqSTVAgAAAIBJSKwCAAABzzAMZWdne947HDnKduTI4cjxDMvOzpZhGGaEBwAA4HPp6enavHmz3G6313C3261NmzZp7969JkUGAKhKKSkpcjqdXsNIqgUAAAAA89AxOwAACGiGYSghIUH79+/3DJuWMK/QdPHx8YqKilJycrIsFos/QwQAAPC5jRs3ymKxFJk4brFYlJaWppiYGBMiAwBUlbyk2oLykmqHDBnCvh8AAAAA/IyKVQAAIKA5HA6vpKqSZGRkyOFwVHFEAAAAVW/AgAHFVuM0DEMDBw70c0QAgKqWl1RblLykWgAAAACAf1GxCgAAVBuPzx4ve0hwoeGOHKfuuD/V/wEBAABUkZiYGMXGxmrLli1e3QFarVb17t1b0dHRJkYH+E9ubq6CgoLMDgPwiwEDBhSbPEVSLQAAAACYg4pVAACg2rCHBMtuL+JVRLIVAABAdTdlyhQFB3uf5wQHB2vy5MkmRQT41759+zRq1Ch9/vnnZocC+EVeUq3V6t1sb7Va1adPH5JqAQAAAMAEJFYBAAAAAAAEoIiICI0bN85r2Pjx4xUREWFSRID/uFwuLVy4UCdPntTChQvlcrnMDgnwC5JqAQAAACCwkFgFAAAAAAAQoOLi4tSqVStJUqtWrXTFFVeYHBHgH2vWrNFPP/0kSTp8+LDWrl1rckSAf5BUCwAAAACBhcQqAAAAAACAAGWz2TRjxgzVq1dPM2fOlM1mMzskoMplZmZq2bJlXsNSU1OVmZlpUkSAf5FUCwAAAACBg8QqAAAQUAzDUHZ2ttcrjyPHKYejiFeO0zNNwXkNwzDjawAAAPhMdHS0Vq1apS5dupgdCuAXKSkpcjqdXsOcTqcWL15sUkSAf+Ul1datW5ekWgAAAAAwGVdkAAAgYBiGoYSEBO3fv7/I8Xfcn1rqMuLj473eR0VFKTk5WRaLxRchAgAAmCIoKMjsEAC/SE9P1+bNmwsNd7vd2rRpk4YMGaKYmBgTIgMAAAAAALURFasAAEDAcDgcxSZVVVRGRoYcDodPlwkAAACgamzcuLHYhyIsFovS0tL8HBHgfy6XSwsXLtTff/+thQsXyuVymR0SAAAAANRaVKwCAAAB6YE7higkpOKVGXJycjXr8fU+jAgAAABAVRswYECxyVOGYWjgwIF+jgjwvzVr1uinn36SJB0+fFhr167ViBEjTI4KAAAAAGonKlYBAICAFBISJHuIrcKvyiRlAQAAADBHTEyMYmNjZbV6N1tarVb16dNH0dHRJkUG+EdmZqaWLVvmNSw1NVWZmZkmRQQAAAAAtRuJVQAAAAAAAAACxpQpUxQcHOw1LDg4WJMnTzYpIsB/UlJS5HQ6vYY5nU4tXrzYpIgAAAAAoHajK0AA8DPDMORwOEqcJjs72/N/h9NVqc/LP3/+5RbFbrfLYrFU6vMAAAAAAKiMiIgIjRs3ziuRZPz48YqIiDAxKqDqpaena/PmzYWGu91ubdq0SUOGDFFMTIwJkQEAAABA7UViFQD4kWEYSkhI0P79+8s8z9RnN/rs8+Pj40scHxUVpeTkZJKrAAAAAACmiouL0xtvvKFjx46padOmuuKKK8wOCahyGzdulMVikWEYhcZZLBalpaWRWAUAAAAAfkZXgADgRw6Ho1xJVf6WkZFRajUtAAAAAAD8oajkEqAmGzBgQLHrvWEYGjhwoJ8jAgAAAABQsQoATPLU9V1ltwVGfqvD5db0l3ebHQYAAAAAAJKkNWvW6LfffpMkHTt2TGvXrtWIESNMjgqoWjExMYqNjdVHH33klWBlsVgUGxur6OhoE6MDAAAAgNqJxCoAMIndZpU9OMjsMAAAAAAACCiZmZlKTU31GrZ06VLFxsYqIiLCnKAAP5kyZYo+/vhj5ebmeoZZrVZNnjzZxKgAAAAAoPYKjFIpAAAAQC1nGIays7O9XnlycpxyOIp+5eQ4PdMVnJ/ucwAAQHWUkpIip9PpNczpdGrx4sUmRQSYy2KxmB0CAAAAANRaVKwCAAB+YxiGHA5HseO9E0lclfqs/PPnX25x7HY7jdUwjWEYSkhI0P79+4scP/veZWVaTnx8vNf7qKgoJScns24DAIBqIz09XZs3by403DAMbdq0SUOGDFFMTIwJkQH+kZKSUugBCbfbrcWLFysxMdGkqAAAAACg9iKxCgAA+EVpiSMFzXr8HZ99dsFkk6KQgAIzORyOMm8b5ZGRkSGHw6HQ0FCfLxsAAKAqbNy4scTxaWlpJFahxiousdDtdpNYCAAAAAAmIbEKAAD4RVUljvgKCSgIFLffM1jBIUGVWoYzJ1fJ8971UUQAAAD+c+aZZ5Y4/uyzz/ZTJID/bdy4URaLpcguvS0WC4mFAAAAAGACEqsAAIDf/eumrgoJtpodhiQpx+nWw8/tNjsMwCM4JEghIZymAwCA2um7774rcfy3337rp0gA/xswYIDS0tKKHGcYhgYOHOjniAAAAAAA3LEBAAB+FxJsVUhw5SryAAAAAKh5SkoskURiCWq0mJgYxcbGasuWLXK73Z7hVqtVvXv3VnR0tInRAQAAAEDtFBilIgAAAAAAAADUenmJJRaLxWu4xWJRnz59SCxBjTdlyhQFBwd7DQsODtbkyZNNiggAAAAAajcSqwAAAAAAAAAEjKISS0JCQkgsQa0QERGhcePGeQ0bP368IiIiTIoIAAAAAGo3EqsAAAAAAAAABIyIiAiNHz/eaxiJJahN4uLi1KpVK0lSq1atdMUVV5gcEeBfOTk5ZocAAAAAeNjMDgAAqgPDMORwOCq9nOzsbM//Hc7cSi/PV/LHkj/GyrLb7YW6b0D14qt1X/Jet3ICaP3PqYL1n3UfAAAAqJy4uDitX79ehw8fJrEEtY7NZtOMGTOUlJSkmTNnymajGR+1xxtvvKHnnntOU6dO1fDhw80OBwAAAJDFMAzD7CBqgqysLIWHh+v48eMKCwszOxwAPmQYhhISErR//36zQ6l2oqKilJycTIJJNcW6X3Gs+yiv7OxsxcXFSZLuuW+oQkIqd+MkJ8eleXPeliStWbNGoaGhlY4RAADA3/bt26ekpCTNnj1bXbp0MTscwO9yc3MVFBRkdhiA32RnZ2vkyJFyuVyy2Wx6/fXXuZ4FAACA6XjUBQBK4XA4SCypoIyMDDkcDhpAqinW/Ypj3UdBpVV/86roluOq9OflX0ZpldiosAYAAAJB/vMlwzCUnZ2tZs2aadGiRapbt67+/PNPz3kL5y8AUDPNnTtXLtfp61mXy6V58+YpKSnJ3KAAAABQ65FYBQDl8MTwtrLbaLwtjcNlaMab35sdBnzotmtbKph1v1ROl6EFy382OwwEmPJWf5s/7z2ffn58fHyJ46mwBgAAzFbe8yXOX1AbULENtc0333yj7du3ew3btm2bvvnmG7Vv396kqAAAAAASqwCgXOw2i+w2q9lhVANuswOAjwXbLAoJZt0vHes+Cgv06m9UWAMAAGYr7/kS5y+o6VwulxYuXKiTJ09q4cKFSklJkc1GUz5qtuIqU82ePVsvv/yyf4MBAAAA8uFqDAAAAPCTG6fFKDhAkhSdTreef3qv2WEAAAB4uWHeQwoOCSlynDMnR0vv+befIwL8b82aNfrpp58kSYcPH9batWs1YsQIk6MCqs7rr7+uY8eOFTnu119/1erVq9kGAAAAYBoSqwAAAAA/CQ62KjgkyOwwAAAAAlZwSIiC7XazwwBMk5mZqWXLlnkNS01NVWxsrCIiIkyKCqha69atK3H8W2+9RWIVAAAATBMYj8sDAAAAAAAAAFDLpaSkyOl0eg1zOp1avHixSREBVW/YsGEljr/iiiv8FAlgrtzcXLNDAAAARaBiFYCAYhiGHA6H2WF4yc7O9vzf4XKbGEn1kf93yv/7BQq73S6LxWJ2GAAAoJxyc3MVFETVN9ROrP8AUPOlp6dr8+bNhYa73W5t2rRJQ4YMUUxMjAmRAVVrxIgRWr16dZHdATZr1kxXXnmlCVEB/rVv3z4lJSVp9uzZ6tKli9nhAACAfEisAhAwDMNQQkKC9u/fb3YoxZrx5g9mh1DtxMfHmx1CIVFRUUpOTia5CgCAaoRGZtRmrP8AUDts3LhRFotFhmEUGmexWJSWlkZiFWqspKQkTZs2rdDw+++/34RoAP9yuVxauHChTp48qYULFyolJUU2G7dwAQAIFHQFCCBgOByOgE6qQs2RkZERcJXRAABA8Qo2MrtcLrNDAvyG9R8Aao8BAwYUmVQlnX4gceDAgX6OCPCf9u3b6+KLL/Ya1qNHD7Vv396kiAD/WbNmjX766SdJ0uHDh7V27VqTIwIAAPmR7gwgICX3ryd7ENWE4FuOXEO3bzxpdhgAAKCcimpkHjFihMlRAf7B+g8AtUdMTIxiY2O1ZcsWud1uz3Cr1arevXsrOjraxOiAqpeYmKgRI0Z4ukC+5557zA4JqHKZmZlaunSp17AXX3xRsbGxioiIMCkqAACQH4lVAAKSPcgiu43EKgAAgNouMzNTy5Yt8xqWmppKIzNqBdZ/AKh9pkyZom3btiknJ8czzGazafLkySZGBfiHzWZT/fr1dfz4cTVo0ICu0FArpKSkyOl0eg1zOp1avHixEhMTTYoKAADkx1kpAAABwDCMgOueMDs72/P/HKe7hCmRJ//vlP/3CxR2u10WC0mrZeWr7TL/uuB05lZ6eb6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\n" ] }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "code", "source": [ "Source = pd.get_dummies(data[[\"Source\"]], drop_first= True)\n", "\n", "Source.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "d1m_vz9gzkAF", "outputId": "775ecc7a-e164-4540-ba0e-5ed2e8c2082a" }, "execution_count": 26, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Source_Chennai Source_Delhi Source_Kolkata Source_Mumbai\n", "0 0 0 0 0\n", "1 0 0 1 0\n", "2 0 1 0 0\n", "3 0 0 1 0\n", "4 0 0 0 0" ], "text/html": [ "\n", "\n", "
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AirlineSourceDestinationRouteTotal_StopsAdditional_InfoPriceJourney_dayJourney_monthDep_hourDep_minArrival_hourArrival_minDuration_hoursDuration_mins
0IndiGoBangloreNew DelhiBLR → DELnon-stopNo info38972432220110250
1Air IndiaKolkataBangloreCCU → IXR → BBI → BLR2 stopsNo info7662155501315725
2Jet AirwaysDelhiCochinDEL → LKO → BOM → COK2 stopsNo info1388296925425190
3IndiGoKolkataBangloreCCU → NAG → BLR1 stopNo info62181251852330525
4IndiGoBangloreNew DelhiBLR → NAG → DEL1 stopNo info133021316502135445
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\n" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "markdown", "source": [ "Source is a Nominal categorical data as we can't assign them any order" ], "metadata": { "id": "e7TfU-Ex0GXu" } }, { "cell_type": "code", "source": [ "# Additional_Info contains almost 80% no_info\n", "# Route and Total_Stops are related to each other\n", "\n", "data.drop([\"Route\", \"Additional_Info\"],axis = 1,inplace = True)" ], "metadata": { "id": "6K4HZyDQ0VUD" }, "execution_count": 28, "outputs": [] }, { "cell_type": "code", "source": [ "data.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4},inplace = True)" ], "metadata": { "id": "NKMlbZ8Q0dp-" }, "execution_count": 29, "outputs": [] }, { "cell_type": "code", "source": [ "#Now adding encoded columns to the dataframe\n", "data_encoded = pd.concat([data, Airline, Source, Destination], axis = 1)" ], "metadata": { "id": "1dxtdYdz09zL" }, "execution_count": 30, "outputs": [] }, { "cell_type": "code", "source": [ "data_encoded.drop([\"Airline\", \"Source\", \"Destination\"], axis = 1, inplace = True)" ], "metadata": { "id": "UzIoo0OL5f-7" }, "execution_count": 31, "outputs": [] }, { "cell_type": "code", "source": [ "data_encoded.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 317 }, "id": "cOA8Xw7h1XBm", "outputId": "9e883f83-fc0f-4815-bc28-6805f883e9de" }, "execution_count": 32, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Total_Stops Price Journey_day Journey_month Dep_hour Dep_min \\\n", "0 0 3897 24 3 22 20 \n", "1 2 7662 1 5 5 50 \n", "2 2 13882 9 6 9 25 \n", "3 1 6218 12 5 18 5 \n", "4 1 13302 1 3 16 50 \n", "\n", " Arrival_hour Arrival_min Duration_hours Duration_mins ... \\\n", "0 1 10 2 50 ... \n", "1 13 15 7 25 ... \n", "2 4 25 19 0 ... \n", "3 23 30 5 25 ... \n", "4 21 35 4 45 ... \n", "\n", " Airline_Vistara Premium economy Source_Chennai Source_Delhi \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " Source_Kolkata Source_Mumbai Destination_Cochin Destination_Delhi \\\n", "0 0 0 0 0 \n", "1 1 0 0 0 \n", "2 0 0 1 0 \n", "3 1 0 0 0 \n", "4 0 0 0 0 \n", "\n", " Destination_Hyderabad Destination_Kolkata Destination_New Delhi \n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 1 \n", "\n", "[5 rows x 30 columns]" ], "text/html": [ "\n", "\n", "
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\n" ] }, "metadata": {}, "execution_count": 32 } ] }, { "cell_type": "markdown", "source": [ "There are 33 columns total so training will take much time so selecting some features" ], "metadata": { "id": "600IAmjT2gds" } }, { "cell_type": "code", "source": [ "X = data_encoded.loc[:, ['Total_Stops', 'Journey_day', 'Journey_month', 'Dep_hour',\n", " 'Dep_min', 'Arrival_hour', 'Arrival_min', 'Duration_hours',\n", " 'Duration_mins', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n", " 'Airline_Jet Airways', 'Airline_Jet Airways Business',\n", " 'Airline_Multiple carriers',\n", " 'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n", " 'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n", " 'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n", " 'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n", " 'Destination_Kolkata', 'Destination_New Delhi']]\n", "X.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 317 }, "id": "2tQ514tN1Z1B", "outputId": "a263007a-584f-4359-b066-83cc81aaca4c" }, "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Total_Stops Journey_day Journey_month Dep_hour Dep_min Arrival_hour \\\n", "0 0 24 3 22 20 1 \n", "1 2 1 5 5 50 13 \n", "2 2 9 6 9 25 4 \n", "3 1 12 5 18 5 23 \n", "4 1 1 3 16 50 21 \n", "\n", " Arrival_min Duration_hours Duration_mins Airline_Air India ... \\\n", "0 10 2 50 0 ... \n", "1 15 7 25 1 ... \n", "2 25 19 0 0 ... \n", "3 30 5 25 0 ... \n", "4 35 4 45 0 ... \n", "\n", " Airline_Vistara Premium economy Source_Chennai Source_Delhi \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " Source_Kolkata Source_Mumbai Destination_Cochin Destination_Delhi \\\n", "0 0 0 0 0 \n", "1 1 0 0 0 \n", "2 0 0 1 0 \n", "3 1 0 0 0 \n", "4 0 0 0 0 \n", "\n", " Destination_Hyderabad Destination_Kolkata Destination_New Delhi \n", "0 0 0 1 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 1 \n", "\n", "[5 rows x 29 columns]" ], "text/html": [ "\n", "\n", "
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\n" ] }, "metadata": {}, "execution_count": 33 } ] }, { "cell_type": "markdown", "source": [ "# Implementing the model" ], "metadata": { "id": "dvzlsYIa4I5X" } }, { "cell_type": "markdown", "source": [ "## Decision Trees" ], "metadata": { "id": "lU3jMty0YHil" } }, { "cell_type": "code", "source": [ "y = data_encoded.iloc[:, 1]\n", "y.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PD85rRw_nhwA", "outputId": "b85e6623-336d-4a42-b902-2a10de6df0a9" }, "execution_count": 34, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 3897\n", "1 7662\n", "2 13882\n", "3 6218\n", "4 13302\n", "Name: Price, dtype: int64" ] }, "metadata": {}, "execution_count": 34 } ] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)" ], "metadata": { "id": "SIqacpuJncsJ" }, "execution_count": 35, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "dtr=DecisionTreeRegressor()\n", "dtr.fit(X_train,y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "UeA4wMJZYJv-", "outputId": "fa2c28e5-b139-41a0-9712-ba4b3fc7d934" }, "execution_count": 36, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DecisionTreeRegressor()" ], "text/html": [ "
DecisionTreeRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "code", "source": [ "y_dtc_pred =dtr.predict(X_test)" ], "metadata": { "id": "pBKYvSEgYanB" }, "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ "sns.distplot(y_dtc_pred)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 626 }, "id": "qhXiiz-xiR1m", "outputId": "8f9a6995-cc4e-4ae2-9029-cc9386caeefd" }, "execution_count": 38, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(y_dtc_pred)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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GFRIREV0fwxi5tQvmagBAdBffZh8b7KfB0l/1AwC8lXYCe3I5XElERO2PYYzc2sVSx03ZI3TeN2jZuEnxkXhgaCRsdoHff3IAJeU1rVkeERHRDTGMkVu7WOroGYvQtex+oAqFAi9NGogeXf1gtFRjwcaDnD9GRETtimGM3FZlTR1Kq6wAWt4zBgB+WjX+Nv0W+Hip8OPJYjy9+RBsvJk4ERG1E4YxclsX6nvFQvw08PZS3dRz9Q0PxNr/uQVeKgX+fegCln+VBd6cgoiI2gPDGLmtm50vdrXb+4ThzYfjoVAA//tzHp7fksUeMiIianOy3iic6Gbc7Hyxxtw7xICqWhue/fwwNmTkoaKmDsO7h1zz5uNEREQ3iz1j5LacPWOGVuoZc3p4RBTWTBkKtVKBLw9ewKZ9+ewhIyKiNsMwRm7JarOjqMyxDEVEUOv1jDndO8SAdf8zDF4qBbLOl+KzzHzYOYeMiIjaAMMYuaUCSzXsAvDVqBDo3Taj7Un99Xh32i1QKoBD50qx9fCFNnkdIiLq3BjGyC0554sZdD5QKNpuPtddA8IxeUQ0AODn0yYcOV/aZq9FRESdE8MYuSVjfRgLb+X5Yo0ZFKnDuN5dAQCf7z8HU0Vtm78mERF1Hgxj5JZKKhzzxbr6a9vl9ZL66dE9xBc1dXZs2pvH+WNERNRqGMbILZWUO3qnQvw17fJ6KqUCk0dEQatWIv9SFY5esLTL6xIRkedjGCO3Y7MLXKp0hLEufu0TxgAgyFeD0XGhAIDUYwVc7oKIiFoFwxi5ndIqK+wCUCsVCPTxatfXHt0zFL4aFYrLa3Aw/1K7vjYREXkmhjFyO875YsF+Gijb8ErKxmi9VLi9fjJ/WnYh6mz2dn19IiLyPAxj5HacVzO25xDllRJ6dEGgtxrmKiuyOHeMiIhuEsMYuR3n5H25wpiXSonhMSEAgP1nOVRJREQ3h2GM3I6zZyxEpjAGALdEBwMAThWVw1zJdceIiKjlGMbI7TjnjHVppzXGGhPip0GPUD8IAPvz2DtGREQtxzBGbkUI0SF6xgBgWHdH79j+PDMXgSUiohZjGCO3UlZdB6tNQKkAgnzbd1mLqw0w6KBVK2GqqMWZkgpZayEiIvfFMEZupaS+VyzIVwO1Ut4fX41aiUGROgDAkXO8gTgREbUMwxi5FVP9fDG5hyid+kcEAgByjGUQHKokIqIWYBgjtyL3shZX69HVH2qlAuYqKwrKauQuh4iI3BDDGLmVkg4yed9Jo1aiR1c/AI7eMSIiouZiGCO3cnn1ffmWtbhan3DnUCVX4yciouZjGCO34lxgNdhP3ispr9RXHwAAyDNVoqrWJnM1RETkbhjGyG1YbXZU1IcdnU/HCWPBfhqEBWhhF8AvhRyqJCKi5mEYI7dhqbICALxUCvh4qWSuxlWfcEfvGOeNERFRczGMkdsw14cxnY8XFAqFzNW46l0/VHm6qJxLXBARUbMwjJHbsFwRxjqa6BBfqBQKWKrrcKnSKnc5RETkRhjGyG2USmGsYyxrcSUvlRLdgn0AALnFvDUSERE1HcMYuQ1zB+4ZA4CYUMd6Y2cYxoiIqBkYxshtlNYP/wV10DAWWx/GcnnTcCIiagaGMXIbzmHKwA4axqJDfKGAY2FaY2m13OUQEZGbYBgjtyHNGfPtmGHM20sFQ5Bj3tieMyaZqyEiInfBMEZuobbOjiqrY8HXjjpMCQAxXXwBAHtyS2SuhIiI3AXDGLkFZ6+YVq2Edwdb8PVKzkn8e3LZM0ZERE3DMEZuobSDX0npFNPFEcZ+KSiX7qNJRER0PQxj5BZKqxzBpqOHMT+tGl38HOugHcw3y1sMERG5BYYxcgvu0jMGAFEhjnljDGNERNQUDGPkFtwpjDlX4mcYIyKipmAYI7fgTmEsKtjRM3Yo38ybhhMR0Q3JHsbeffddxMTEwNvbGwkJCdizZ89122/evBl9+/aFt7c3Bg0ahG3btrnsF0Jg2bJliIiIgI+PD5KSknDixAmXNiaTCdOnT0dgYCCCgoIwe/ZslJeXu7Q5fPgwxowZA29vb0RFRWHVqlUNannrrbfQp08f+Pj4ICoqCn/4wx9QXc3FPtuCubJjrzF2pQidNzQqJS5VWpFnqpS7HCIi6uBkDWObNm3CwoULsXz5cuzfvx9DhgxBcnIyCgsLG22/e/duTJ06FbNnz8aBAwcwadIkTJo0CVlZWVKbVatWYc2aNVi3bh0yMjLg5+eH5ORkl5A0ffp0HD16FKmpqdi6dSt27dqFuXPnSvstFgvuuusudO/eHZmZmXjttdewYsUKvPfee1KbDRs2YPHixVi+fDmys7PxwQcfYNOmTXjuuefa4DtFUs+Yd8cPY2qVEv0MgQA4VElERDemEDKOoyQkJGDEiBF45513AAB2ux1RUVF48sknsXjx4gbtJ0+ejIqKCmzdulXaNmrUKMTHx2PdunUQQsBgMODpp5/GokWLAAClpaXQ6/VYv349pkyZguzsbPTv3x979+7F8OHDAQApKSmYOHEizp07B4PBgLVr1+L555+H0WiERuO4Mm7x4sXYsmULjh8/DgCYP38+srOzkZaWJtXy9NNPIyMjAz/++GOj51tTU4Oamhrp7xaLBVFRUSgtLUVgYODNfCs9xoaMvAbbaqw2vLj1GABg+b39oVXf3Dpj0xKib7qmG/mloAzrd5/Bo7fGYMV9A5p9PBERdVwWiwU6na7VPr9l6xmrra1FZmYmkpKSLhejVCIpKQnp6emNHpOenu7SHgCSk5Ol9rm5uTAajS5tdDodEhISpDbp6ekICgqSghgAJCUlQalUIiMjQ2ozduxYKYg5XycnJweXLl0CANx6663IzMyUhlVPnz6Nbdu2YeLEidc855UrV0Kn00mPqKioG3+jCKXVjl4xby/lTQex9hIfFQSAPWNERHRjsoWx4uJi2Gw26PV6l+16vR5Go7HRY4xG43XbO7/eqE1YWJjLfrVajZCQEJc2jT3Hla8xbdo0/OlPf8Lo0aPh5eWFnj174vbbb7/uMOWSJUtQWloqPfLz86/Zli4rq64DAARoO/4QpZMzjB27YEFNnU3eYoiIqEOTfQK/u9q5cydefvll/O1vf8P+/fvx+eef4+uvv8af//znax6j1WoRGBjo8qAbk8KYt1rmSpquexdfBPt6odZmR/bFMrnLISKiDky2MBYaGgqVSoWCggKX7QUFBQgPD2/0mPDw8Ou2d369UZurLxCoq6uDyWRyadPYc1z5GkuXLsUjjzyCxx57DIMGDcIDDzyAl19+GStXroTdbm/aN4GapKx+mNKdwphCocDgbkEAgMPnzLLWQkREHZtsYUyj0WDYsGEuE+DtdjvS0tKQmJjY6DGJiYku7QEgNTVVah8bG4vw8HCXNhaLBRkZGVKbxMREmM1mZGZmSm127NgBu92OhIQEqc2uXbtgtVpdXqdPnz4IDg4GAFRWVkKpdP32qVSO+UxcW6p1Xe4Zc59hSgAY0k0HADh8rlTmSoiIqCOTdZhy4cKFeP/99/HRRx8hOzsbTzzxBCoqKjBr1iwAwIwZM7BkyRKp/YIFC5CSkoI33ngDx48fx4oVK7Bv3z7Mnz8fgKM34qmnnsJLL72Er776CkeOHMGMGTNgMBgwadIkAEC/fv0wYcIEzJkzB3v27MFPP/2E+fPnY8qUKTAYDAAc88E0Gg1mz56No0ePYtOmTVi9ejUWLlwo1XLvvfdi7dq12LhxI3Jzc5GamoqlS5fi3nvvlUIZtQ537BkDgEH1PWNHGMaIiOg6ZP10mzx5MoqKirBs2TIYjUbEx8cjJSVFmiyfl5fn0vt06623YsOGDXjhhRfw3HPPIS4uDlu2bMHAgQOlNs888wwqKiowd+5cmM1mjB49GikpKfD29pbafPzxx5g/fz7Gjx8PpVKJhx56CGvWrJH263Q6bN++HfPmzcOwYcMQGhqKZcuWuaxF9sILL0ChUOCFF17A+fPn0bVrV9x77734y1/+0pbfsk7JXXvGBtf3jJ0oLENlbR18Ne4VJomIqH3Ius5YZ9fa65R4gsbW9Ppr6i8oKq/B7NGx6NnV/6Zfoz3WGXO+xsi//AeFZTX47PFEDI8JafbzEBFRx+Mx64wRNVVZjXsOUwKXe8c4b4yIiK6FYYw6NKvNjmqr4+rUQDcbpgSAQZFBAIAj5xnGiIiocQxj1KE554t5qRTQqt3vx/Vyz5hZ3kKIiKjDcr9PN+pULl9J6QWFQiFzNc03MNIRxk4XV0jnQkREdCWGMerQLNKtkNxvvhgAdA3QwqDzhhDA0QsWucshIqIOiGGMOjR3XWPsSoPqhyq53hgRETWGYYw6NHddY+xK0m2ROImfiIgawTBGHZo73iT8aoMinT1jZnkLISKiDolhjDq0KyfwuytnGDtTUonSSk7iJyIiVwxj1KF5Qs9YsJ8GUSE+AICsCxyqJCIiVwxj1KFZPGACPwAMrl/8lSvxExHR1RjGqMOqs9tRWWsD4N7DlMAVV1SeN8tbCBERdTgMY9RhldcPUSoVgK9GJXM1N4f3qCQiomthGKMO68plLZRuuPr+lZwr8Z+7VAVTRa3M1RARUUfCMEYdlidM3ncK9PZCj1A/ALxpOBERuWIYow6rrMYxed/fTW+FdLXLK/Gb5S2EiIg6FM/4lCOPVFHj6Blr7TC2ISOvVZ+vqQZF6vDlwQucN0ZERC7YM0YdVrkzjHnAMCVw+bZIB/PNEELIWwwREXUYDGPUYTmvpvSYYcpIHVRKBQrLanCxtFrucoiIqINgGKMOq7yNhinl4qNRoV9EAADgQJ5Z3mKIiKjDYBijDsvTwhgADI0KBgDsz7skcyVERNRRMIxRh+WRYSw6CABwgGGMiIjqMYxRh1Rns6PaagfgORP4AeCWaEfPWNYFC2rqbDJXQ0REHQHDGHVIzl4xpQLw8XLvWyFdqXsXXwT7eqG2zo7si2Vyl0NERB2A53Q5kEe5cohS4ea3QrqSQqHA0Ohg7DheiAN5lxAfFSR3SdfUkvXYpiVEt0ElRESejT1j1CF54nwxp6H1AWw/r6gkIiIwjFEHVeFhC75e6ZbujnljnMRPREQAwxh1UJ624OuVBnfTQaEAzl2qQoGFi78SEXV2LQpjp0+fbu06iFx48jBlgLcX+kcEAgB+Pl0iczVERCS3FoWxXr164Y477sD//u//orqa/7On1ldWH8b8PDCMAcCtPbsAANJPMYwREXV2LQpj+/fvx+DBg7Fw4UKEh4fjt7/9Lfbs2dPatVEn5sk9YwCQ6Axj7BkjIur0WhTG4uPjsXr1aly4cAH//Oc/cfHiRYwePRoDBw7Em2++iaKiotaukzoZT57ADwAjYkKgUipwtqQS581VcpdDREQyuqkJ/Gq1Gg8++CA2b96MV199FSdPnsSiRYsQFRWFGTNm4OLFi61VJ3UynjyBH3DMGxsUqQPAoUoios7upsLYvn378Lvf/Q4RERF48803sWjRIpw6dQqpqam4cOEC7r///taqkzoRm12gstZxqyBPDWPA5aHK3aeKZa6EiIjk1KJPujfffBMffvghcnJyMHHiRPzrX//CxIkToVQ6sl1sbCzWr1+PmJiY1qyVOonK2joIAAoAvhoPDmM9umDtzlP4+VQJhBDNvtNAc1fI5+r4REQdU4s+6dauXYvf/OY3ePTRRxEREdFom7CwMHzwwQc3VRx1Ts7J+74aFVRKz7kV0tWGxwTDS6XAhdJq5Jkq0b2Ln9wlERGRDFo0TJmamopnn322QRATQiAvz/G/dY1Gg5kzZ958hdTplHv45H0nX40aQ6Mcq/F//wsveiEi6qxaFMZ69uyJ4uKG81xMJhNiY2Nvuijq3Dx98v6VxvcLAwCkZBllroSIiOTSojAmhGh0e3l5Oby9vW+qIKJyD1/w9Up3D3T0LmfkmlBSXiNzNUREJIdmfdotXLgQAKBQKLBs2TL4+vpK+2w2GzIyMhAfH9+qBVLn4wxjAZ0gjEV38cUAQyCOXrAg9VgBpozkJHsios6mWZ92Bw4cAODoGTty5Ag0Go20T6PRYMiQIVi0aFHrVkidToWHr75/tbsHhuPoBQu+yTIyjBERdULN+rT77rvvAACzZs3C6tWrERgY2CZFUefWWSbwO00YGIHXt/+C3aeKUVplhc7HS+6SiIioHbXo0+7DDz9s7TqIJM4J/J1hzhgA9ArzR1yYP04UliMtuwAP3tJN7pJaRVPWTmvuWmkA10sjIs/T5E+7Bx98EOvXr0dgYCAefPDB67b9/PPPb7ow6rw8/Sbhjbl7UAROpJ3Apr35bhvGrDY7PvgxF1sOnMfF0iqYKmoxqFsQpo2Mwr1DDB69gC8R0c1o8r+OOp1O+l+uTqdrs4Koc7ML0SnD2NSRUVi78yQyck3IPHsJw7oHN/s5zJW1OFlYjjxTJfy0akQG+SAm1K9dvo9Hzpdi25GLKK2yumw/lG/GoXwz1qSdxMePJSAmlAvbEhFdrcn/Sl85NMlhSmor1VYb7PUrp3SmMBah88EDQyPx6b5zWLvzFP4xc3iTjy2rtmLr4Ys4cr60wT6NWonkAeFIiA1pzXIlQgikHS/EjuOFAIDwQG/M+69euCU6CH4aNVKOGvGv3Wdw3lyFh/+ejg1zRqFXmH+b1EJE5K5atM5YVVUVKisrpb+fPXsWb731FrZv395qhVHn5Jwv5u2lhFp1U/exdzu/HdcTCgXwn+wC5BjLmnTMgbxL+Ot/fsGR86VQAIgO8cW43l0xIiYYXf21qK2z49+HLuD9H06jqKx11zGz2QU2Z56TgtiYXqHY+cfb8cio7hhg0CEm1A+Pj+uJL+ePRh99AArLajDlvXRcMFe1ah1ERO6uRV0P999/Px588EE8/vjjMJvNGDlyJDQaDYqLi/Hmm2/iiSeeaO06qZPwlCHKltzEu2dXf0wYEI5vsox457uTeHvq0Gu2t9rs+PfhC0g/VQIAMAR548Gh3WAI8pHa2IXAnlwTUo4acbakEpPfS8eGx0YhXHfzCzPbhcD/7T+Hg/lmKBXA/fGRGBETAm8vVYO2XQO0+GTuKEz/RwayL1qw7MssvD9jeLNvjE5E5Kla1PWwf/9+jBkzBgDw2WefITw8HGfPnsW//vUvrFmzplULpM7FU8JYS/3u9l4AgH8fuoD/l36m0TYFlmo88kGGFMT+q28YnhjXyyWIAYBSocCoHl3w5B29EOTjhdNFFXj47+k4f5M9U0IIbD18QQpi0xO6Y0TM9YdBQ/w0WDMlHl4qBf6TXYhvj/L2T0RETi0KY5WVlQgICAAAbN++HQ8++CCUSiVGjRqFs2fPNuu53n33XcTExMDb2xsJCQnYs2fPddtv3rwZffv2hbe3NwYNGoRt27a57BdCYNmyZYiIiICPjw+SkpJw4sQJlzYmkwnTp09HYGAggoKCMHv2bJSXl7u0OXz4MMaMGQNvb29ERUVh1apVDWoxm82YN28eIiIioNVq0bt37wb1UPN09jA2qJsOi+7qDQBY/tVRfJdTKO0TQiD1WAEmvLULP582QaNW4n8SopHUTw+V8tq9TF38tZgztgeiQ3yRZ6rEI//IuKlbL/0nuwA/nzZBAeDXw6LQL6Jp6w3G6QPw+Lie0rlZqq03OIKIqHNoURjr1asXtmzZgvz8fHz77be46667AACFhYXNWgh206ZNWLhwIZYvX479+/djyJAhSE5ORmFhYaPtd+/ejalTp2L27Nk4cOAAJk2ahEmTJiErK0tqs2rVKqxZswbr1q1DRkYG/Pz8kJycjOrqaqnN9OnTcfToUaSmpmLr1q3YtWsX5s6dK+23WCy466670L17d2RmZuK1117DihUr8N5770ltamtrceedd+LMmTP47LPPkJOTg/fffx+RkZFNPn9qqLMt+NqYeXf0wq+HdYNdAI99tA/T3v8ZL209hqQ3v8ecf+3DpUor+kcE4ne390R/Q9OubA721WDTb0chMsgHp4sr8OiHe6XvdXP8cKII3+UUAQDuHWJAfFRQs46fd0cvxHTxRYGlBut2nmr26xMReSKFuNZdv6/js88+w7Rp02Cz2TB+/Hhp4v7KlSuxa9cufPPNN016noSEBIwYMQLvvPMOAMButyMqKgpPPvkkFi9e3KD95MmTUVFRga1bt0rbRo0ahfj4eKxbtw5CCBgMBjz99NPSbZlKS0uh1+uxfv16TJkyBdnZ2ejfvz/27t2L4cMdV6ylpKRg4sSJOHfuHAwGA9auXYvnn38eRqNRuuXT4sWLsWXLFhw/fhwAsG7dOrz22ms4fvw4vLxatmK6xWKBTqdDaWkp72ZQ79drd2Pf2UsY3y8M4/vq5S6n3Vy9kGltnR3zN+zH9mMFLts1KiUevS0GT9/VG/+Xeb7Zr3G6qBz/vS4dJRW1GBkbgg8fHXHNxXWvnveWkVuCLw9eAAAk99djXJ+wG55HY1KyjHj8fzOh8/HCH5J6Q6Nu3v8JuegrEcmttT+/W9Qz9utf/xp5eXnYt28fUlJSpO3jx4/HX//61yY9R21tLTIzM5GUlHS5GKUSSUlJSE9Pb/SY9PR0l/YAkJycLLXPzc2F0Wh0aaPT6ZCQkCC1SU9PR1BQkBTEACApKQlKpRIZGRlSm7Fjx7rcezM5ORk5OTm4dOkSAOCrr75CYmIi5s2bB71ej4EDB+Lll1+GzWa75jnX1NTAYrG4PMhVZx+mdNKolXhvxnDsXHQ7np/YDw8P74a/Th6CfUuT8NzEftCqG06Ub4oeXf3x0W9GIkCrxp5cEx79cE+Tesh+OFEkBbGxcaGNBrGmurO/HtEhviitsuJA/qUWPw8Rkado8doB4eHhGDp0KJTKy08xcuRI9O3bt0nHFxcXw2azQa937f3Q6/UwGhuf3Gs0Gq/b3vn1Rm3Cwlw/SNRqNUJCQlzaNPYcV77G6dOn8dlnn8Fms2Hbtm1YunQp3njjDbz00kvXPOeVK1dCp9NJj6ioqGu27aycwSCgk4cxp5hQP8wZ2wOrfj0EDwzthkDvm79v5cBIHf7fYwkI8FZj75lLmP6PDOSbKhttW2e3IyXLiG+yHD/3Y+O6InlA+E29vkqpwKO3xgAAfjpZAnvzO+eJiDxKiz7xKioq8MorryAtLQ2FhYWw2+0u+0+fPt0qxXVkdrsdYWFheO+996BSqTBs2DCcP38er732GpYvX97oMUuWLMHChQulv1ssFgayq7BnrH3ERwVhw2Oj8D8fZOBQvhkT3tqF5+7ph/vjI+GvVcNuF8gtrsC/D12A0eKYb3lXfz1uv0GPWFOX9FAA0KqVKC6vwYmCcvQJD7jZUyIiclst+sR77LHH8P333+ORRx5BREREi9YLCg0NhUqlQkGB65yYgoIChIc3/j/v8PDw67Z3fi0oKEBERIRLm/j4eKnN1RcI1NXVwWQyuTxPY69z5WtERETAy8sLKtXl4aJ+/frBaDSitrbWZYjTSavVQqvVNnpu5LhasEKawH/zPUB0fYO66fDv+aPx9OaD2HvmEp7/IgvLvjyKuDB/nL9UhbL698JXo8J9QwwY3C2o1V5b66XCiJgQ/HiyGOmnixnGiKhTa1EY++abb/D111/jtttua/ELazQaDBs2DGlpaZg0aRIAR29TWloa5s+f3+gxiYmJSEtLw1NPPSVtS01NRWJiIgAgNjYW4eHhSEtLk8KXxWJBRkaGtBBtYmIizGYzMjMzMWzYMADAjh07YLfbkZCQILV5/vnnYbVapcn5qamp6NOnD4KDHfcMvO2227BhwwbY7XZpqPaXX35BREREo0GMbqyi1garzTFk5adt2Zwoap7oLr7YODcRH/6Ui/W7z+DcpSocr1/9X6NSYlCkDhMGhl9zkv/NSIh1hLGTheWoqKlrk9cgInIHLfrXLzg4GCEhN3+vu4ULF2LmzJkYPnw4Ro4cibfeegsVFRWYNWsWAGDGjBmIjIzEypUrAQALFizAuHHj8MYbb+Cee+7Bxo0bsW/fPmnJCYVCgaeeegovvfQS4uLiEBsbi6VLl8JgMEiBr1+/fpgwYQLmzJmDdevWwWq1Yv78+ZgyZQoMBgMAYNq0aXjxxRcxe/ZsPPvss8jKysLq1atdLk544okn8M4772DBggV48sknceLECbz88sv4/e9/f9Pfl86quP52PV4qRYsnqFPzqZQKPDamBx4b0wP5pkocvVCK6BA/ZJ69dN31y25WF38tDEHeuGCuxrELFoxoo/tnEhF1dC0KY3/+85+xbNkyfPTRR/D19W3xi0+ePBlFRUVYtmwZjEYj4uPjkZKSIk2Wz8vLc7lA4NZbb8WGDRvwwgsv4LnnnkNcXBy2bNmCgQMHSm2eeeYZVFRUYO7cuTCbzRg9ejRSUlLg7X35FjAff/wx5s+fj/Hjx0OpVOKhhx5yuXOATqfD9u3bMW/ePAwbNgyhoaFYtmyZy1pkUVFR+Pbbb/GHP/wBgwcPRmRkJBYsWIBnn322xd+Pzq64fiFSzheTT1SIL6JCHL/TB/PNbf56gyKDcMFsxJHzpQxjRNRptWidsaFDh+LUqVMQQiAmJqbBOlv79+9vtQI9GdcZc+Vcfyoq2AdP1N8WqLNoydpZLbn/ZVs+f0uYKmrx+vYcKAAsmdivSUGc64wRkdxa+/O7RV0QziE/otbEnrHOJ8RPg8ggH5w3V+HYBQtGsneMiDqhFn3qXWvpBqKbIYWxTnwrpM5oYKQO581VOHLezDBGRJ1Sixd9NZvN+Mc//oElS5bAZDIBcAxPnj/fvFu0EDmxZ6xzGhTpuL/m6aIKVLbgfplERO6uRZ96hw8fRlJSEnQ6Hc6cOYM5c+YgJCQEn3/+OfLy8vCvf/2rteukTqC4rBYAw1hnE+KnQViAFoVlNThVXCGFMyKizqJFPWMLFy7Eo48+ihMnTrhcpThx4kTs2rWr1YqjzqWkwjlMyQVfO5u4MH8AwMnCMpkrISJqfy3qgti7dy/+/ve/N9geGRl5zftKEt1IcbmjZ4wLvraN9rg6sqV6hfnjp1MlOFFYDiFEi+7qQUTkrlrUM6bVamGxWBps/+WXX9C1a9ebLoo6J+eirxym7HxiQ/2hUihgrrSipKJW7nKIiNpVi8LYfffdhz/96U+wWq0AHCvf5+Xl4dlnn8VDDz3UqgVS51BttUn3QgzQcpiys9GolYju4lhs9mRhuczVEBG1rxaFsTfeeAPl5eXo2rUrqqqqMG7cOPTq1QsBAQH4y1/+0to1UifgvJJSpVDA26vFF/mSG7s8b4xhjIg6lxaNB+l0OqSmpuKnn37CoUOHUF5ejltuuQVJSUmtXR91EiVXzBfjfKHOqVeYP7YfK8CponLY7KJN74tJRNSRNDuM2e12rF+/Hp9//jnOnDkDhUKB2NhYhIeHc+IttRgXfCVDkA98vFSostpw/lIlorv4yV0SEVG7aNZ4kBAC9913Hx577DGcP38egwYNwoABA3D27Fk8+uijeOCBB9qqTvJwXPCVlAoFenR1BLDckkqZqyEiaj/N+uRbv349du3ahbS0NNxxxx0u+3bs2IFJkybhX//6F2bMmNGqRZLncy5r4c/J+51a9xBfHL1gQV5JBQBemU1EnUOzesY++eQTPPfccw2CGAD813/9FxYvXoyPP/641YqjzqOIy1oQgO71Q5NnTZUQQshcDRFR+2hWGDt8+DAmTJhwzf133303Dh06dNNFUefjXFvKnwu+dmoRQd5QKxWorLVJF3UQEXm6ZoUxk8kEvV5/zf16vR6XLl266aKo85EWfOUE/k5NrVSiW7APAOCsqULmaoiI2kezwpjNZoNafe0PS5VKhbq6upsuijqfyxP4OWess4sOqR+q5CR+IuokmtUNIYTAo48+Cq1W2+j+mpqaVimKOh9eTUlO3bv4Aicc88aIiDqDZn3yzZw584ZteCUlNVedzY5LlY5ba/Em4RQd4rgtUlFZDSpr6+CrYUAnIs/WrH/lPvzww7aqgzoxU/3kfaUC8GPPWKfnp1Uj1F+L4vIa5Jkq0Tc8UO6SiIjaFG8CSLIrqh+iDPHTQMk7OBAc640BnDdGRJ0DwxjJzrnga6h/43MRqfNxDlWev1QlcyVERG2PYYxk51zWgmGMnCLrl7c4Z+bir0Tk+RjGSHYlFY4w1sVfI3Ml1FGEBWqhVipQbbVLcwqJiDwVwxjJjsOUdDW1UolwnTcA4LyZQ5VE5NkYxkh2HKakxkQGOYYqGcaIyNNxHQGSnfNqylB/Day2zjk/aENGntwldDhSGOMkfiLycOwZI9lxmJIa45zEf95cBTsn8RORB2MYI9mVlHOYkhoKC/CGWqlATZ0dpnJO4iciz8UwRrKy2wVK6q+WCw3g1ZR0mUqpQET9JP5znDdGRB6MYYxkZa6ywmZ3DEF18WPPGLmKDHYs/nqBYYyIPBjDGMmquH6IMtBbDY2aP47kqlv9JP5zl3hbJCLyXPz0I1k5l7UIC/SWuRLqiAz1YexiaTUn8RORx2IYI1kV1oexrpy8T43oGqCFqn4Sv7nSKnc5RERtgmGMZFXkDGMBDGPUkEqpQFj9z4axlPPGiMgzMYyRrJwLvjKM0bU4r6i8aKmWuRIiorbBMEayYs8Y3Uh4/XxCYynDGBF5JoYxkpUzjIUxjNE1hOsuT+InIvJEDGMkK/aM0Y2E1w9TmipqUVNnk7kaIqLWxzBGsuKcMboRf60aAd5qAEABe8eIyAMxjJFsrDY7TPW3QuLSFnQ9nMRPRJ6MYYxkU1J/82eVUoFgX96Xkq4tPNAxb4yT+InIEzGMkWyc88VC/TVQKhUyV0MdmXPeGCfxE5EnYhgj2RSWOT5YwwJ4KyS6PucwZYGlGnY7b4tERJ6FYYxkwyspqalC/S/fFuncJa7ET0SehWGMZFPE+1JSE6mUCujrQ/uxixaZqyEial0MYyQbLmtBzeFc/PW4kWGMiDwLwxjJhsOU1BzOeWPZ7BkjIg/DMEayYRij5nBeUXncWCZzJURErYthjGTjHKbkfSmpKSLqbxh+tqQS5TV1MldDRNR6OkQYe/fddxETEwNvb28kJCRgz549122/efNm9O3bF97e3hg0aBC2bdvmsl8IgWXLliEiIgI+Pj5ISkrCiRMnXNqYTCZMnz4dgYGBCAoKwuzZs1FeXu7S5vDhwxgzZgy8vb0RFRWFVatWXbOmjRs3QqFQYNKkSc07+U6MPWPUHL5aNQLrb4uUw3ljRORBZA9jmzZtwsKFC7F8+XLs378fQ4YMQXJyMgoLCxttv3v3bkydOhWzZ8/GgQMHMGnSJEyaNAlZWVlSm1WrVmHNmjVYt24dMjIy4Ofnh+TkZFRXX14wcvr06Th69ChSU1OxdetW7Nq1C3PnzpX2WywW3HXXXejevTsyMzPx2muvYcWKFXjvvfca1HTmzBksWrQIY8aMacXvjGerqKlDZa3jps+hvJqSmihcmjfGoUoi8hwKIYSsKygmJCRgxIgReOeddwAAdrsdUVFRePLJJ7F48eIG7SdPnoyKigps3bpV2jZq1CjEx8dj3bp1EELAYDDg6aefxqJFiwAApaWl0Ov1WL9+PaZMmYLs7Gz0798fe/fuxfDhwwEAKSkpmDhxIs6dOweDwYC1a9fi+eefh9FohEbjuFXP4sWLsWXLFhw/flx6bZvNhrFjx+I3v/kNfvjhB5jNZmzZsqVJ526xWKDT6VBaWorAwMAWff/c1ZniCtz++k74aVQ4+qcJ0vYNGXkyVkUd3bdHjfj+lyJMT4jGXx4YJHc5RNRJtfbnt6w9Y7W1tcjMzERSUpK0TalUIikpCenp6Y0ek56e7tIeAJKTk6X2ubm5MBqNLm10Oh0SEhKkNunp6QgKCpKCGAAkJSVBqVQiIyNDajN27FgpiDlfJycnB5cuXZK2/elPf0JYWBhmz559w/OtqamBxWJxeXRWhRyipBYID+QVlUTkeWQNY8XFxbDZbNDr9S7b9Xo9jEZjo8cYjcbrtnd+vVGbsLAwl/1qtRohISEubRp7jitf48cff8QHH3yA999/v0nnu3LlSuh0OukRFRXVpOM8EeeLUUs4hylzjGW8LRIReQzZ54y5q7KyMjzyyCN4//33ERoa2qRjlixZgtLSUumRn5/fxlV2XEX196VkGKPmCPXXQqNWoqLWhvxLlXKXQ0TUKtRyvnhoaChUKhUKCgpcthcUFCA8PLzRY8LDw6/b3vm1oKAAERERLm3i4+OlNldfIFBXVweTyeTyPI29jnPfqVOncObMGdx7773SfrvdDsDRy5aTk4OePXu6HK/VaqHVMnwAQEGZc1kL3iScmk6lVKC33h9Z5y3IvmhB9y5+cpdERHTTZO0Z02g0GDZsGNLS0qRtdrsdaWlpSExMbPSYxMREl/YAkJqaKrWPjY1FeHi4SxuLxYKMjAypTWJiIsxmMzIzM6U2O3bsgN1uR0JCgtRm165dsFqtLq/Tp08fBAcHo2/fvjhy5AgOHjwoPe677z7ccccdOHjwYKcegmyKglJHz5hz2ImoqfqFOybLcvFXIvIUsvaMAcDChQsxc+ZMDB8+HCNHjsRbb72FiooKzJo1CwAwY8YMREZGYuXKlQCABQsWYNy4cXjjjTdwzz33YOPGjdi3b5+05IRCocBTTz2Fl156CXFxcYiNjcXSpUthMBikNcD69euHCRMmYM6cOVi3bh2sVivmz5+PKVOmwGAwAACmTZuGF198EbNnz8azzz6LrKwsrF69Gn/9618BAN7e3hg4cKDLuQQFBQFAg+3UUEH9MKU+kD2F1Dx9I+rDGJe3ICIPIXsYmzx5MoqKirBs2TIYjUbEx8cjJSVFmiyfl5cHpfJyB96tt96KDRs24IUXXsBzzz2HuLg4bNmyxSUAPfPMM6ioqMDcuXNhNpsxevRopKSkwNv7ci/Mxx9/jPnz52P8+PFQKpV46KGHsGbNGmm/TqfD9u3bMW/ePAwbNgyhoaFYtmyZy1pk1HLGUmcYY88YNU+/8AAAvGE4EXkO2dcZ68w68zpjg5Z/i7KaOqQ9PQ49u/pL27nOGN1I8gA9hr30HygUQNaKZPhpZf8/JRF1Mh61zhh1ThU1dSirv7cge8aoubr4axEWoIUQQE4BhyqJyP0xjFG7K7A4hij9tWr4s1eDWoDzxojIkzCMUbszWjh5n24O540RkSdhGKN2V2hxrDHGIUpqqb4R9WGMPWNE5AEYxqjdOXvGwhnGqIX61q81lm20gNcgEZG7YxijduecMxbGMEYt1LOrP7xUCpRV1+FC/TIpRETuimGM2l2B1DPGOWPUMhq1UloS5fhFzhsjIvfGMEbtroBzxqgV9IvgbZGIyDMwjFG7k1bf530p6Sb0rb+iMps9Y0Tk5hjGqF0JIVBYxlsh0c3ry54xIvIQDGPUrkwVtbDaHFe/hQVwzhi1nHOtsdNF5ai22mSuhoio5RjGqF0554uF+mvgpeKPH7Vc1wAtQvw0sAvgZGG53OUQEbUYPw2pXRVYOERJrUOhUHDeGBF5BIYxaldGhjFqRc7FXzlvjIjcGcMYtSv2jFFrkm6LxHtUEpEbYxijdlXAm4RTK+rnvC3SxTLeFomI3BbDGLUraY0x9oxRK4jT+0OpcFylW1ReI3c5REQtwjBG7epifRgzBPnIXAl5Am8vFWJD/QAAxy9y3hgRuSeGMWpX581VAIDIIPaMUeu4vPgr540RkXtiGKN2Y6m2oqy6DgB7xqj1OBd/Zc8YEbkrhjFqNxfNjiHKYF8v+GrUMldDnsK5vMUxrjVGRG6KYYzazYX6IUr2ilFrci5vcbKwHDV1vC0SEbkfhjFqN+cYxqgNRAb5IMjXC3V2gRMFvC0SEbkfhjFqNxekyfsMY9R6FAoFBhgcQ5VZ50tlroaIqPkYxqjdXB6m5JWU1LoGGnQAgKMXOG+MiNwPwxi1G84Zo7YyINIRxrIusGeMiNwPwxi1mwtmLvhKbWOgwXlbJAtsdt4WiYjcC8MYtYs6mx3G+vtScs4YtbaYLn7w06hQbbXjdBEn8RORe2EYo3ZRWFYDm13AS6VAV3/eJJxal1KpQH/nJH4OVRKRm2EYo3bhnC8WrvOGUqmQuRryRAPqJ/FnneckfiJyLwxj1C6c96Q06DhESW3DubzFUfaMEZGbYRijduGcvM/5YtRWBtZfUXn0vAV2TuInIjfCMEbtgstaUFvrFeYPjVqJspo65Jkq5S6HiKjJGMaoXTCMUVvzUinRL8IxVHmYK/ETkRthGKN2cZ6r71M7iO/mGKo8lG+WtxAiomZgGKN2cZ73paR2EB8dBAA4yDBGRG6EYYzanLmyFmXVdQCAbsG+MldDnmxItyAAjhuGW212eYshImoihjFqc2dLHJOpwwK08NGoZK6GPFlMFz8EeqtRU2dHjrFM7nKIiJqEYYza3Nn6K9u6d2GvGLUtpVKBIVFBADhUSUTug2GM2lxeSQUAIDrET+ZKqDOIrw9jnMRPRO6CYYzanHOYkj1j1B6c88YOnTPLWgcRUVMxjFGb4zAltSfnMOWJwnKUVVvlLYaIqAkYxqjN5dX3jEWHMIxR2+saoEVkkA+EAI5w8VcicgMMY9Smqq02GC2O+1J278I5Y9Q+nPPGDuSZZa2DiKgpGMaoTeXXD1EGaNUI9vWSuRrqLIZ1DwYA7D1jkrkSIqIbYxijNuWcvB/dxRcKhULmaqizGBkbAgDIPHMJNruQuRoioutTy10AeTZO3qfWtiEj74Zt7EJAq1airKYOx40WDDDo2qEyIqKWYc8YtSmuMUZyUCoU0gUje3M5VElEHRvDGLUp9oyRXGJCHf8B2HvmksyVEBFdH8MYtSnnshbduawFtbOY+qt395wxQQjOGyOijqtDhLF3330XMTEx8Pb2RkJCAvbs2XPd9ps3b0bfvn3h7e2NQYMGYdu2bS77hRBYtmwZIiIi4OPjg6SkJJw4ccKljclkwvTp0xEYGIigoCDMnj0b5eXlLm0OHz6MMWPGwNvbG1FRUVi1apXL/vfffx9jxoxBcHAwgoODkZSUdMPaOxObXSD/0uUJ/ETtqVuwD1RKBYrKaqQLSYiIOiLZw9imTZuwcOFCLF++HPv378eQIUOQnJyMwsLCRtvv3r0bU6dOxezZs3HgwAFMmjQJkyZNQlZWltRm1apVWLNmDdatW4eMjAz4+fkhOTkZ1dXVUpvp06fj6NGjSE1NxdatW7Fr1y7MnTtX2m+xWHDXXXehe/fuyMzMxGuvvYYVK1bgvffek9rs3LkTU6dOxXfffYf09HRERUXhrrvuwvnz59vgO+V+LpZWwWoT8FIpEKHzkbsc6mS8VEpEBjl+7vZwiQsi6sAUQub++4SEBIwYMQLvvPMOAMButyMqKgpPPvkkFi9e3KD95MmTUVFRga1bt0rbRo0ahfj4eKxbtw5CCBgMBjz99NNYtGgRAKC0tBR6vR7r16/HlClTkJ2djf79+2Pv3r0YPnw4ACAlJQUTJ07EuXPnYDAYsHbtWjz//PMwGo3QaDQAgMWLF2PLli04fvx4o+dis9kQHByMd955BzNmzGiwv6amBjU1NdLfLRYLoqKiUFpaisDAwBZ+BzuuH08U438+yECPrn7Y8fTtTTqmKVfKETVVSpYRu04U4b+HdcNr/z1E7nKIyENYLBbodLpW+/yWtWestrYWmZmZSEpKkrYplUokJSUhPT290WPS09Nd2gNAcnKy1D43NxdGo9GljU6nQ0JCgtQmPT0dQUFBUhADgKSkJCiVSmRkZEhtxo4dKwUx5+vk5OTg0qXGJwRXVlbCarUiJCSk0f0rV66ETqeTHlFRUdf83niCE4VlAIBeXf1lroQ6q9j6Sfy7T5Vw3hgRdViyhrHi4mLYbDbo9XqX7Xq9HkajsdFjjEbjdds7v96oTVhYmMt+tVqNkJAQlzaNPceVr3G1Z599FgaDoUFYdFqyZAlKS0ulR35+fqPtPMXJQsccvDg9wxjJIzbUDxqVEufNVcgtrpC7HCKiRnHR11byyiuvYOPGjdi5cye8vb0bbaPVaqHVatu5MvmccIaxsACZK6HOSqNWYlj3YKSfLsGPJ4vRg720RNQBydozFhoaCpVKhYKCApftBQUFCA8Pb/SY8PDw67Z3fr1Rm6svEKirq4PJZHJp09hzXPkaTq+//jpeeeUVbN++HYMHD77+SXcizp6xXmH8ACT5jI4LBQD8cKJY5kqIiBonaxjTaDQYNmwY0tLSpG12ux1paWlITExs9JjExESX9gCQmpoqtY+NjUV4eLhLG4vFgoyMDKlNYmIizGYzMjMzpTY7duyA3W5HQkKC1GbXrl2wWq0ur9OnTx8EBwdL21atWoU///nPSElJcZmD1tmVlNfAVFELhQLoyd4IktHYuK4AgPRTJbDa7DJXQ0TUkOxLWyxcuBDvv/8+PvroI2RnZ+OJJ55ARUUFZs2aBQCYMWMGlixZIrVfsGABUlJS8MYbb+D48eNYsWIF9u3bh/nz5wMAFAoFnnrqKbz00kv46quvcOTIEcyYMQMGgwGTJk0CAPTr1w8TJkzAnDlzsGfPHvz000+YP38+pkyZAoPBAACYNm0aNBoNZs+ejaNHj2LTpk1YvXo1Fi5cKNXy6quvYunSpfjnP/+JmJgYGI1GGI3GBuuVdUbOIcqoYF/4aFQyV0Od2QBDIIJ9vVBeU4dD+Wa5yyEiakD2OWOTJ09GUVERli1bBqPRiPj4eKSkpEiT5fPy8qBUXs6Mt956KzZs2IAXXngBzz33HOLi4rBlyxYMHDhQavPMM8+goqICc+fOhdlsxujRo5GSkuIyl+vjjz/G/PnzMX78eCiVSjz00ENYs2aNtF+n02H79u2YN28ehg0bhtDQUCxbtsxlLbK1a9eitrYWv/71r13Oafny5VixYkVrf6vcyuX5YuwVI3kplQrc2isUXx++iF0nijE8pvGrnYmI5CL7OmOdWWuvU9KRLP8yCx+ln8Vvx/XAkrv7Nfk4rjNGrW1aQjQ27c3Ds/93BLdEB+Hz390md0lE5OY8ap0x8lzOnjGuMUYdwej6eWMH880wVdTKXA0RkSuGMWoT0jClnstakPwig3zQLyIQdgGkZRfc+AAionYk+5wx8jzmyloUlTlu+3Tg7CUcu2CRuSIiIHmAHtkXLdh+rAD/Pdyz735BRO6FPWPU6pzrixl03tB68UpK6hju6u9YH/CHE0WoqrXJXA0R0WUMY9TqpMVeOURJHUi/iAB0C/ZBtdWO738pkrscIiIJwxi1uuyLjmHJ3lzWgjoQhUKB5AGO3rHtxxq/vywRkRwYxqjVHTlfCgAYGKmTuRIiV3f1d6xfmJZdyNX4iajDYBijVlVns+NYfc8Ywxh1NMNjQhDip0FplRXpp0rkLoeICADDGLWyU0UVqLba4adRoUeon9zlELlQKRW4e6BjqHLLgfMyV0NE5MAwRq3KOUQ5wKCDUqmQuRqihh68pRsAIOWoERU1dTJXQ0TEMEat7Mg5MwBgUDcOUVLHdEt0EGK6+KKy1oZvj3IiPxHJj2GMWpWzZ2wQ54tRB6VQKDBpaCQA4AsOVRJRB8AwRq2Gk/fJXTw41DFU+ePJYhhLq2Wuhog6O4YxajWcvE/uIrqLL4Z3D4YQ7B0jIvkxjFGr4eR9cicP19+f8uOMs7DZhczVEFFnxjBGrSaLi72SG7kv3oAgXy+cu1SFtOwCucshok6MYYxazcF8MwBgULdAeQshagJvLxUmj3D0jn2UfkbeYoioU2MYo1ZRUVMnDVMO7x4iczVETfM/Cd2hVAA/nSzBycIyucshok5KLXcB5Bkyz16CzS4QGeSDqBBfucshkmzIyLvu/j7hgci+aMHzX2Th/njHkhfTEqLbozQiIgDsGaNW8vNpx33+EnqwV4zcy609uwBw/IfCUm2VuRoi6owYxqhVZOSaAACjenSRuRKi5ukR6ofoEF/U2QV++KVI7nKIqBNiGKObVllbh8P1t0FKZBgjN6NQKPBffcMAAHvOmFDG3jEiamcMY3TT9p81w2oTMOi80S3YR+5yiJotLswf3YJ9YLUJ/HiiWO5yiKiTYRijm5aR65gvNqpHFygUXOyV3I9CocD4+t6xn3NLeIskImpXDGN00zh5nzxBb30AokN8YbUJrEo5Lnc5RNSJMIzRTamoqcOhfMf6YgmxnC9G7kuhUOBXgyMAAJ8fOI/9eZdkroiIOguGMbop3/9ShFqbHTFdfNG9C9cXI/fWLdgXt0QHAwBe/Pcx2HnPSiJqBwxjdFO2HzUCAO4aEM75YuQR7hqgh59GhUP5Znyy9/oLxhIRtQaGMWqx2jo70o4XAgCSB+hlroaodQR6e+EPd/YGALz8dTbyTZUyV0REno5hjFosI7cEZdV1CPXXIj4qWO5yiFrNrNtiMSImGBW1Njzz2WEOVxJRm2IYoxbbfrQAAHBn/zColByiJM+hUirw2q+HwMdLhfTTJfhw9xm5SyIiD8YwRi1itwtsP1Y/X6x/uMzVELW+mFA/LJnYFwCwcls29tTf8ouIqLUxjFGLHDxnRoGlBn4aFRJ7ckkL8kyPjOqOe4cYUGcX+N3H+1Fg4WKwRNT6GMaoRTbtyQcA3NlfD28vlczVELUNhUKBVx8ahD76ABSX12Duv/ahoqZO7rKIyMMwjFGzWaqt+OrQBQDA1JHRMldD1LZ8NWr8/ZFhCPL1wqFzpfjt/8tETZ1N7rKIyIMwjFGzfXngPKqsNvQK88fIWN4CiTxfTKgfPnx0BHw1Kvx4shhPbTwIq80ud1lE5CEYxqhZhBD4OMOxEOa0kdFc6JU6jaHRwXjvkeHQqJT4JsuI3/6/TFTVsoeMiG4ewxg1y4F8M44by6BVK/HQLd3kLoeoXY2OC8XfHxkGby8ldhwvxP98kAFTRa3cZRGRm2MYo2b5xw+nAQD3DI6AztdL5mqI2t8dfcPwv7MTEOitRubZS7j37R9x+JxZ7rKIyI0xjFGTHTlXim1HjFAogN+O7Sl3OUSyGR4Tgs+euBWxoX44b67Cr9em46PdZ7hSPxG1iFruAsh9rPr2OADggfhI9AkPkLkaorazIaNpNwh/ZFR3bN6Xj2xjGZZ/dRTfHjVi1a8Ho1uwbxtX2Hk09b1wmpbAK7zJ/bBnjJok/VQJfjhRDLVSgaeSestdDlGH4O2lwvRR3bHi3v7w9lJi96kS3PnmLryz4wSqrZzcT0RNwzBGN2S12fGXbccAONYVi+7C//UTOSkVCjx6WyxSFozFyNgQVFlteH37L7jzr9/js8xzqOMSGER0AwxjdENr0k4g67wFOh8vPDm+l9zlEHVIMaF+2DR3FFZPiUdYgBb5pios2nwId/51Fz7fz1BGRNfGOWN0XZlnTXj3u5MAgJcfGISwAG+ZKyLqeK6e1/TE7T2RcdqEXSeKkFtcgYWfHsLL27IxNq4rBncLwqO3xchTqBurs9tRUFoDo6UapopalFbVoqbODqvNDrVSCa1aiQBvNVRKoFdYAAYYAnmrNnIbDGN0TYVl1Viw8SDsAnjwlkjcMzhC7pKI3IJWrcLY3l2R0CMEP58qwa4TxSgur8XnB87jmywjzl2qxCOJ3dG9i5/cpXZYdrtA1oVS7DheiBOFZTh/qQp1TbhaddeJYgCARqXEoG46jOvdFckDwtFb789FqqnDUggheC22TCwWC3Q6HUpLSxEYGCh3OS5MFbWY+t7PyCkoQ1SID77+/RgEejd/XbHmXglF5IlqrDbsOWPCz6dLcKnSCgBQKIAxcV0xKd6AO/vrEdCC3y9PU1RWgx9OFOH7X4rww4niBgvq+nipYAjyRoifFsG+XvD2UsFLpUCdXaDaaoelygq1SoHsi2UoLq9xOTamiy+SB4QjeWA4hkYFMZjRTWntz2+GMRl11DBWXF6DRz/cg6zzFoQFaLH58cQW/w+eYYzoMrsQ+KWgDGdLKvH9L0XSdo1aidt7d8WvhhhwR5+unSaYlZTXYE+uCRm5jqB63Fjmsj9Aq0ZUiC966wPQs6sfQvw0NwxR0xKiIYRAnqkS6adKsP1YAX48WYzaustz9iKDfHDvEAPuG2JAv4gABjNqNoYxD9IRw9juU46bIBeW1aCLnwabfjsKvcJavqYYwxhRQ9MSonGmuAJbDp7Hvw9dwKmiCmmfUgEMitRhVM8uSOzRBSNiQuCndd8ZJXa7gKXaivPmKpwsLMepwnKcKqpATkEZThaWN2g/KFKHsb1DMa53GIZGB2HzvnM3XUON1YZfCstx9EIpjhvLXIJZ1wAtHhnVHfcOMSA2lMPG1DQMYx6kI4UxY2k13vnuBD7OyIMQQFyYP9b+zy03FcQAhjGixly5MKkQAseNZdh6+AK+OWLE6eIKl7YKBRAd4ou4MH/E6QPQW++PbsG+CPXXomuAFn4aVZv17NjsAuU1daiof5TVfy2vrpO2l9fUobzGdsWf61BWbcWlCitKKmpxqbIWtuvM9eqjD8CoHiFI6NEFI2NDEOqvddnf2v+G1NbZkVNQhsPnzMgxlrnMQ+sbHoBxvbtiXO+uGBYTDK1avgsAWnLeXPC2/XhkGHv33Xfx2muvwWg0YsiQIXj77bcxcuTIa7bfvHkzli5dijNnziAuLg6vvvoqJk6cKO0XQmD58uV4//33YTabcdttt2Ht2rWIi4uT2phMJjz55JP497//DaVSiYceegirV6+Gv7+/1Obw4cOYN28e9u7di65du+LJJ5/EM88806xarkfuMGazC2ScLsGXBy/gi4Pnpf8t/vewbnjx/gHw1dz8/8YZxogaut6H5sXSKqSfKkH6qRLsPlWC8+aq6z6Xj5cKIX4a+GlV8NGo4eulgq9GBS+VEkoloFAooFQooFQ41kRTAKizC1htdtTW2VFrs7v8uaLGhrJqR9CqasWFa0P8NOjZ1Q+9wvzRs6s/eob5Y0i3IIT4aa57XFv+G1JtteHYBQsKy2vw08lil9Do46XCqB4hGNQtCP0jAtAvIhBRwb5QKm8u+AohUFFrg6m8FqbKWpgqalBSXgtzpRU1dTbU2gTqbHYcPlcKAPBSKeGlUsBLpYRGpYTWSwkfjQo+Xpcf3hoVlAoFw1g78rgwtmnTJsyYMQPr1q1DQkIC3nrrLWzevBk5OTkICwtr0H737t0YO3YsVq5ciV/96lfYsGEDXn31Vezfvx8DBw4EALz66qtYuXIlPvroI8TGxmLp0qU4cuQIjh07Bm9vx9IMd999Ny5evIi///3vsFqtmDVrFkaMGIENGzYAcHyje/fujaSkJCxZsgRHjhzBb37zG7z11luYO3duk2u5nvYIY3a7QFlNHSxVjv+l5pkqkVtUgYP5l3Ag3wxz/WRiABgRE4yFd/ZBYs8urfb6DGNEN6es2orCshpEh/jiRGEZThSUo8BSjaKyGlTUts8q/yqFAlovx/IRWrXK8dXrij+rldB6qVz2+2pV8NOo4adVw0+jglrVcZe1nJYQDVNFLX44UYRdvxRj14kiFJXVNGjnr1WjexdfdPHXItRPgy7+GgT5aqC+KqDV1tlhqbairLoOlmorLFV1MNX3EpZU1LoMk7YWrVqJUH8tdD5e0Pl4IdBHLf3ZX+sFpcLRy6pQKBxf4fwK2IVjPqMQQvqzXaD+7+KK/XBpI6S2jhihViqhViqgrg+QaqUSapVC+rOXWgltfaDUSF9V0NT/DLl+vWK7SnnTIbi1eVwYS0hIwIgRI/DOO+8AAOx2O6KiovDkk09i8eLFDdpPnjwZFRUV2Lp1q7Rt1KhRiI+Px7p16yCEgMFgwNNPP41FixYBAEpLS6HX67F+/XpMmTIF2dnZ6N+/P/bu3Yvhw4cDAFJSUjBx4kScO3cOBoMBa9euxfPPPw+j0QiNxvE/t8WLF2PLli04fvx4k2q5Wk1NDWpqLv+Cl5aWIjo6Gvn5+a0axg7lX8Iz/3cYZVWOIYPrXQ2u81Hjzv563DPIgOExwa0+3PHpvvxWfT4iuqy2zobyahsqrTZYbXZYnT1ddXbYhADqPzwF4PgghePPSiigUro+1PVfNfVhyvlBqFUroerAQao1PDw8yuXvQgjkGMuw94wJOcYyx/y2ogpYWzFEab2UCPHVIMjXC8F+GgT5eMHHy9GjqVYpcbqoHAJAnc3Ri2m12VFns6PKake11YZqqw1VVsf2zsBLpYRGrYBG5fjZ1NSHNJVSIX1uKRSOn20pdNZv6633x4r7btxB0hwWiwVRUVEwm83Q6XQ3/4RCRjU1NUKlUokvvvjCZfuMGTPEfffd1+gxUVFR4q9//avLtmXLlonBgwcLIYQ4deqUACAOHDjg0mbs2LHi97//vRBCiA8++EAEBQW57LdarUKlUonPP/9cCCHEI488Iu6//36XNjt27BAAhMlkalItV1u+fLkAwAcffPDBBx98eMAjPz+/0c/75pL1Ep3i4mLYbDbo9XqX7Xq9Xup9uprRaGy0vdFolPY7t12vzdVDoGq1GiEhIS5tYmNjGzyHc19wcPANa7nakiVLsHDhQunvdrsdJpMJXbp0kfXSamfCb+0euo6C5+feeH7uz9PPkefn3lpyfkIIlJWVwWAwtEoN7nu9tBvSarXQal2vFAoKCpKnmEYEBgZ65C+aE8/PvfH83J+nnyPPz7019/xaZXiynqwTAUJDQ6FSqVBQUOCyvaCgAOHh4Y0eEx4eft32zq83alNYWOiyv66uDiaTyaVNY89x5WvcqBYiIiKiG5E1jGk0GgwbNgxpaWnSNrvdjrS0NCQmJjZ6TGJiokt7AEhNTZXax8bGIjw83KWNxWJBRkaG1CYxMRFmsxmZmZlSmx07dsButyMhIUFqs2vXLlitVpfX6dOnD4KDg5tUCxEREdENtcrMs5uwceNGodVqxfr168WxY8fE3LlzRVBQkDAajUIIx0T6xYsXS+1/+uknoVarxeuvvy6ys7PF8uXLhZeXlzhy5IjU5pVXXhFBQUHiyy+/FIcPHxb333+/iI2NFVVVVVKbCRMmiKFDh4qMjAzx448/iri4ODF16lRpv9lsFnq9XjzyyCMiKytLbNy4Ufj6+oq///3vzarFHVRXV4vly5eL6upquUtpEzw/98bzc3+efo48P/fWEc5P9jAmhBBvv/22iI6OFhqNRowcOVL8/PPP0r5x48aJmTNnurT/9NNPRe/evYVGoxEDBgwQX3/9tct+u90uli5dKvR6vdBqtWL8+PEiJyfHpU1JSYmYOnWq8Pf3F4GBgWLWrFmirKzMpc2hQ4fE6NGjhVarFZGRkeKVV15pUPuNaiEiIiK6HtnXGSMiIiLqzDx7JT8iIiKiDo5hjIiIiEhGDGNEREREMmIYIyIiIpIRw1gn9+677yImJgbe3t5ISEjAnj175C6pgRUrVjhu+nrFo2/fvtL+6upqzJs3D126dIG/vz8eeuihBovx5uXl4Z577oGvry/CwsLwxz/+EXV1dS5tdu7ciVtuuQVarRa9evXC+vXr2+ycdu3ahXvvvRcGgwEKhQJbtmxx2S+EwLJlyxAREQEfHx8kJSXhxIkTLm1MJhOmT5+OwMBABAUFYfbs2SgvL3dpc/jwYYwZMwbe3t6IiorCqlWrGtSyefNm9O3bF97e3hg0aBC2bdvW5uf36KOPNnhPJ0yY4Dbnt3LlSowYMQIBAQEICwvDpEmTkJOT49KmPX8uW/v3uCnnd/vttzd4Dx9//HG3OL+1a9di8ODB0orriYmJ+Oabb6T97vzeNeX83Pm9a8wrr7wChUKBp556Strmdu+hzFdzkow2btwoNBqN+Oc//ymOHj0q5syZI4KCgkRBQYHcpblYvny5GDBggLh48aL0KCoqkvY//vjjIioqSqSlpYl9+/aJUaNGiVtvvVXaX1dXJwYOHCiSkpLEgQMHxLZt20RoaKhYsmSJ1Ob06dPC19dXLFy4UBw7dky8/fbbQqVSiZSUlDY5p23btonnn39efP755wKA+OKLL1z2v/LKK0Kn04ktW7aIQ4cOifvuu6/RtfKGDBkifv75Z/HDDz+IXr16uayVV1paKvR6vZg+fbrIysoSn3zyifDx8WmwVp5KpRKrVq0Sx44dEy+88EKrrJV3o/ObOXOmmDBhgst7ajKZXNp05PNLTk4WH374ocjKyhIHDx4UEydOFNHR0aK8vFxq014/l23xe9yU8xs3bpyYM2eOy3tYWlrqFuf31Vdfia+//lr88ssvIicnRzz33HPCy8tLZGVlCSHc+71ryvm583t3tT179oiYmBgxePBgsWDBAmm7u72HDGOd2MiRI8W8efOkv9tsNmEwGMTKlStlrKqh5cuXiyFDhjS6z2w2Cy8vL7F582ZpW3Z2tgAg0tPThRCOYKBUKqWFhIUQYu3atSIwMFDU1NQIIYR45plnxIABA1yee/LkySI5ObmVz6ahq8OK3W4X4eHh4rXXXpO2mc1modVqxSeffCKEEOLYsWMCgNi7d6/U5ptvvhEKhUKcP39eCCHE3/72NxEcHCydoxBCPPvss6JPnz7S3x9++GFxzz33uNSTkJAgfvvb37bZ+QnhCGP333//NY9xp/MTQojCwkIBQHz//fdCiPb9uWyP3+Orz08Ixwf6lR9+V3On8xNCiODgYPGPf/zD4967q89PCM9578rKykRcXJxITU11OSd3fA85TNlJ1dbWIjMzE0lJSdI2pVKJpKQkpKeny1hZ406cOAGDwYAePXpg+vTpyMvLAwBkZmbCarW6nEffvn0RHR0tnUd6ejoGDRoEvV4vtUlOTobFYsHRo0elNlc+h7ONHN+L3NxcGI1Gl3p0Oh0SEhJczikoKAjDhw+X2iQlJUGpVCIjI0NqM3bsWGg0GqlNcnIycnJycOnSJamNXOe9c+dOhIWFoU+fPnjiiSdQUlIi7XO38ystLQUAhISEAGi/n8v2+j2++vycPv74Y4SGhmLgwIFYsmQJKisrpX3ucn42mw0bN25ERUUFEhMTPe69u/r8nDzhvZs3bx7uueeeBnW443uoblZr8hjFxcWw2WwuP4gAoNfrcfz4cZmqalxCQgLWr1+PPn364OLFi3jxxRcxZswYZGVlwWg0QqPRICgoyOUYvV4Po9EIADAajY2ep3Pf9dpYLBZUVVXBx8enjc6uIWdNjdVzZb1hYWEu+9VqNUJCQlzaxMbGNngO577g4OBrnrfzOdrKhAkT8OCDDyI2NhanTp3Cc889h7vvvhvp6elQqVRudX52ux1PPfUUbrvtNgwcOFB6/fb4ubx06VKb/x43dn4AMG3aNHTv3h0GgwGHDx/Gs88+i5ycHHz++educX5HjhxBYmIiqqur4e/vjy+++AL9+/fHwYMHPeK9u9b5Ae7/3gHAxo0bsX//fuzdu7fBPnf8/WMYow7v7rvvlv48ePBgJCQkoHv37vj000/bNSRR65kyZYr050GDBmHw4MHo2bMndu7cifHjx8tYWfPNmzcPWVlZ+PHHH+UupU1c6/zmzp0r/XnQoEGIiIjA+PHjcerUKfTs2bO9y2y2Pn364ODBgygtLcVnn32GmTNn4vvvv5e7rFZzrfPr37+/2793+fn5WLBgAVJTU+Ht7S13Oa2Cw5SdVGhoKFQqVYOrSwoKChAeHi5TVU0TFBSE3r174+TJkwgPD0dtbS3MZrNLmyvPIzw8vNHzdO67XpvAwMB2D3zOmq733oSHh6OwsNBlf11dHUwmU6ucd3v/DPTo0QOhoaE4efKkVJc7nN/8+fOxdetWfPfdd+jWrZu0vb1+Ltv69/ha59eYhIQEAHB5Dzvy+Wk0GvTq1QvDhg3DypUrMWTIEKxevdpj3rtrnV9j3O29y8zMRGFhIW655Rao1Wqo1Wp8//33WLNmDdRqNfR6vdu9hwxjnZRGo8GwYcOQlpYmbbPb7UhLS3OZV9ARlZeX49SpU4iIiMCwYcPg5eXlch45OTnIy8uTziMxMRFHjhxx+XBPTU1FYGCg1G2fmJjo8hzONnJ8L2JjYxEeHu5Sj8ViQUZGhss5mc1mZGZmSm127NgBu90u/cOamJiIXbt2wWq1Sm1SU1PRp08fBAcHS206wnmfO3cOJSUliIiIkOrqyOcnhMD8+fPxxRdfYMeOHQ2GS9vr57Ktfo9vdH6NOXjwIAC4vIcd9fwaY7fbUVNT4/bv3Y3OrzHu9t6NHz8eR44cwcGDB6XH8OHDMX36dOnPbvceNmu6P3mUjRs3Cq1WK9avXy+OHTsm5s6dK4KCglyuLukInn76abFz506Rm5srfvrpJ5GUlCRCQ0NFYWGhEMJxCXN0dLTYsWOH2Ldvn0hMTBSJiYnS8c5LmO+66y5x8OBBkZKSIrp27droJcx//OMfRXZ2tnj33XfbdGmLsrIyceDAAXHgwAEBQLz55pviwIED4uzZs0IIx9IWQUFB4ssvvxSHDx8W999/f6NLWwwdOlRkZGSIH3/8UcTFxbks/WA2m4VerxePPPKIyMrKEhs3bhS+vr4Nln5Qq9Xi9ddfF9nZ2WL58uWtsvTD9c6vrKxMLFq0SKSnp4vc3Fzxn//8R9xyyy0iLi5OVFdXu8X5PfHEE0Kn04mdO3e6LA9QWVkptWmvn8u2+D2+0fmdPHlS/OlPfxL79u0Tubm54ssvvxQ9evQQY8eOdYvzW7x4sfj+++9Fbm6uOHz4sFi8eLFQKBRi+/btQgj3fu9udH7u/t5dy9VXiLrbe8gw1sm9/fbbIjo6Wmg0GjFy5Ejx888/y11SA5MnTxYRERFCo9GIyMhIMXnyZHHy5Elpf1VVlfjd734ngoODha+vr3jggQfExYsXXZ7jzJkz4u677xY+Pj4iNDRUPP3008Jqtbq0+e6770R8fLzQaDSiR48e4sMPP2yzc/ruu+8EgAaPmTNnCiEcy1ssXbpU6PV6odVqxfjx40VOTo7Lc5SUlIipU6cKf39/ERgYKGbNmiXKyspc2hw6dEiMHj1aaLVaERkZKV555ZUGtXz66aeid+/eQqPRiAEDBoivv/66Tc+vsrJS3HXXXaJr167Cy8tLdO/eXcyZM6fBP14d+fwaOzcALj8z7flz2dq/xzc6v7y8PDF27FgREhIitFqt6NWrl/jjH//oslZVRz6/3/zmN6J79+5Co9GIrl27ivHjx0tBTAj3fu9udH7u/t5dy9VhzN3eQ4UQQjSvL42IiIiIWgvnjBERERHJiGGMiIiISEYMY0REREQyYhgjIiIikhHDGBEREZGMGMaIiIiIZMQwRkRERCQjhjEiIiIiGTGMEREREcmIYYyIiIhIRgxjRERERDL6/8rm3Vvg4ULlAAAAAElFTkSuQmCC\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.scatter(y_test, y_dtc_pred, alpha = 0.5)\n", "plt.xlabel(\"y_test\")\n", "plt.ylabel(\"y_pred\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "id": "SMhKm0jciTan", "outputId": "463fd59c-b572-47b4-c6c1-c2dc63b9d307" }, "execution_count": 39, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from sklearn import metrics\n", "metrics.r2_score(y_test, y_dtc_pred)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_JldEFL2Yl68", "outputId": "bc8b8cf1-6018-4421-ad7b-76e1a06a9792" }, "execution_count": 40, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.708256405913855" ] }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "markdown", "source": [ "### HyperParameter Tuning" ], "metadata": { "id": "SdcVyCO3Yotw" } }, { "cell_type": "code", "source": [ "max_depth=[5,10,15,20]\n", "ccp_alpha=[0.001,0.05,0.1]\n", "max_features=[5,10,15,20]" ], "metadata": { "id": "YQmGSv4WYr6H" }, "execution_count": 41, "outputs": [] }, { "cell_type": "code", "source": [ "decision_grid = {'max_depth':max_depth,\n", " 'ccp_alpha':ccp_alpha,\n", " 'max_features':max_features}" ], "metadata": { "id": "TMr9h9DDY6os" }, "execution_count": 42, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import RandomizedSearchCV" ], "metadata": { "id": "beQvcwV9n6s_" }, "execution_count": 43, "outputs": [] }, { "cell_type": "code", "source": [ "decision_random = RandomizedSearchCV(estimator = dtr, param_distributions = decision_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42)" ], "metadata": { "id": "Cov1Cp0iZKZT" }, "execution_count": 44, "outputs": [] }, { "cell_type": "code", "source": [ "decision_random.fit(X_train,y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "-7Mcth-4ZZwB", "outputId": "81cc85b5-edeb-425c-dbc8-cea7dad0f98e" }, "execution_count": 45, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END ........ccp_alpha=0.1, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END ........ccp_alpha=0.1, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END ........ccp_alpha=0.1, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END ........ccp_alpha=0.1, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END ........ccp_alpha=0.1, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=15; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=15; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=15; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=15; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=15; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=15, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=15, max_features=5; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=10, max_features=10; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=10, max_features=10; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=10, max_features=10; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=10, max_features=10; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.1, max_depth=10, max_features=10; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=20, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=20, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=20, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=20, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=20, max_features=5; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=5, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=5, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=5, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=5, max_features=20; total time= 0.0s\n", "[CV] END .......ccp_alpha=0.05, max_depth=5, max_features=20; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=10, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=10, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=10, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=10, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.001, max_depth=10, max_features=5; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=10; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=10; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=10; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=10; total time= 0.0s\n", "[CV] END ......ccp_alpha=0.05, max_depth=15, max_features=10; total time= 0.0s\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "RandomizedSearchCV(cv=5, estimator=DecisionTreeRegressor(),\n", " param_distributions={'ccp_alpha': [0.001, 0.05, 0.1],\n", " 'max_depth': [5, 10, 15, 20],\n", " 'max_features': [5, 10, 15, 20]},\n", " random_state=42, scoring='neg_mean_squared_error',\n", " verbose=2)" ], "text/html": [ "
RandomizedSearchCV(cv=5, estimator=DecisionTreeRegressor(),\n",
              "                   param_distributions={'ccp_alpha': [0.001, 0.05, 0.1],\n",
              "                                        'max_depth': [5, 10, 15, 20],\n",
              "                                        'max_features': [5, 10, 15, 20]},\n",
              "                   random_state=42, scoring='neg_mean_squared_error',\n",
              "                   verbose=2)
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" ] }, "metadata": {}, "execution_count": 45 } ] }, { "cell_type": "code", "source": [ "decision_random.best_params_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fvjHGfBpZm97", "outputId": "56a6aa86-b966-4213-f794-16f9c5316032" }, "execution_count": 46, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'max_features': 20, 'max_depth': 15, 'ccp_alpha': 0.1}" ] }, "metadata": {}, "execution_count": 46 } ] }, { "cell_type": "code", "source": [ "dtr_best=DecisionTreeRegressor(ccp_alpha= 0.1, max_depth= 15, max_features= 20)\n", "dtr_best.fit(X_train,y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "HGK59xvSZd2w", "outputId": "4d41ac5d-aaae-4b43-8798-eb67cab1d724" }, "execution_count": 47, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DecisionTreeRegressor(ccp_alpha=0.1, max_depth=15, max_features=20)" ], "text/html": [ "
DecisionTreeRegressor(ccp_alpha=0.1, max_depth=15, max_features=20)
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" ] }, "metadata": {}, "execution_count": 47 } ] }, { "cell_type": "code", "source": [ "y_dtr_prediction = dtr_best.predict(X_test)" ], "metadata": { "id": "LMkVlcOraAFc" }, "execution_count": 48, "outputs": [] }, { "cell_type": "code", "source": [ "metrics.r2_score(y_test, y_dtr_prediction)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uLFeksmRaLY3", "outputId": "6c2d1ad2-c433-4198-9f41-026d52731e10" }, "execution_count": 49, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.7403542706530073" ] }, "metadata": {}, "execution_count": 49 } ] }, { "cell_type": "markdown", "source": [ "## Random Forest" ], "metadata": { "id": "MDJcd_IxDhnA" } }, { "cell_type": "code", "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "reg_rf = RandomForestRegressor()\n", "reg_rf.fit(X_train, y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "id": "VTsc0_i05MZe", "outputId": "2ea944d6-8832-4717-a964-c919254443d8" }, "execution_count": 50, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "RandomForestRegressor()" ], "text/html": [ "
RandomForestRegressor()
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" ] }, "metadata": {}, "execution_count": 50 } ] }, { "cell_type": "code", "source": [ "y_pred = reg_rf.predict(X_test)" ], "metadata": { "id": "-lPqg4dR5NCc" }, "execution_count": 51, "outputs": [] }, { "cell_type": "code", "source": [ "reg_rf.score(X_train, y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M0UVMbNL502p", "outputId": "c9269ebd-a4a0-497f-99c6-5cb4c24c8891" }, "execution_count": 52, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.9532995307064535" ] }, "metadata": {}, "execution_count": 52 } ] }, { "cell_type": "code", "source": [ "reg_rf.score(X_test,y_test)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "p1avttHX54FI", "outputId": "63a2d9c5-4b17-4d5a-a109-94df584f6509" }, "execution_count": 53, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8117505754702412" ] }, "metadata": {}, "execution_count": 53 } ] }, { "cell_type": "code", "source": [ "sns.distplot(y_test-y_pred)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 645 }, "id": "_o5FcrE857VH", "outputId": "5fccd7ec-ab9f-4a02-dcae-effb4ec81115" }, "execution_count": 54, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(y_test-y_pred)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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kVFVrgbk+XDU1x+sWgxcRkVdh8CKS0Q+v02jHOV5ERN6JwYtIRtIZjfXXabTrUt/7VclLBhEReRUGLyIZ3TA3nt8FcI4XEZG3YvAiklFTZzQCDT1eNXVW1Fmtbq8XERG5BoMXkYxKK2sBAL4+KoftWh8l7AOP7PUiIvIeDF5EMjJX2ybPa9WOH0WlQgFfntlIROR1GLyIZFRRf9ai7gc9XgDneREReSMGLyIZ2Xu8NOrGH0XpzEYuKUFE5DUYvIhkZK629Wb9cKgRaJj3xaFGIiLvweBFJKMKaY4XhxqJiDoDBi8iGVVUNT25HgC09T1e1XVcToKIyFsweBHJyFzTQvCq31ZTxx4vIiJvweBFJCP7UKPGp/FH0T7hvsbCHi8iIm/B4EUkI3MLc7w0KtvHk0ONRETeg8GLSEYtndXYMNTI4EVE5C0YvIhkIoRocY6XhsGLiMjrMHgRyaSyxgIhbF83OdTIOV5ERF6HwYtIJvb5XUoF4KNSNNpvD16c40VE5D0YvIhkYj+j0U+jhkLROHhpVbZeMA41EhF5DwYvIpnYJ9b769RN7uccLyIi78PgRSQTqcdLy+BFRNRZMHgRycR8h+BlP9PRIgTDFxGRl2DwIpKJfSkJf23jMxoBwEfV8PGsrC9LRESejcGLSCblVQ2T65uiUiqgVtom3ZtreL1GIiJvwOBFJBP7UKN/M0ONQMM8r8pq9ngREXkDBi8imdxpjhfQMM+LPV5ERN6BwYtIJhX1y0m0FLzY40VE5F0YvIhk0jDU2PTkegDQqNjjRUTkTRi8iGRSUdOaoUZbKONZjURE3oHBi0gmrZnjZR9qtK9yT0REno3Bi0gmbTqrkT1eRERegcGLSCb2yfUtBi8Ve7yIiLwJgxeRTNoy1MgeLyIi78DgRSSTtgw1mhm8iIi8AoMXkUwqpB6v5peT0ErreHGokYjIGzB4Ecmg1mJFdZ0VAHu8iIg6EwYvIhmYb1uJvsU5Xir7HC/2eBEReQMGLyIZ2IcZNWolfFTNfwylazXykkFERF6BwYtIBuZWLCUBABpp5Xr2eBEReQMGLyIZtGZiPcA5XkRE3obBi0gG0hpemjv1ePGsRiIib8LgRSSD1qzhBQBaFXu8iIi8CYMXkQwqWrFqPdDQ41VVa4XFKlxeLyIici0GLyIZtLbHyx68AF42iIjIGzB4EcnAXNO6sxrVSgWUCtvXPLORiMjzMXgRyaC1Q40KhaLhzEau5UVE5PHaFby+//57Z9eDqFNpGGpseTkJgKvXExF5k3YFr/79++Pee+/Fu+++i6qqKmfXicjrtbbHC2hYRJU9XkREnq9dwevQoUMYMWIEkpOTERYWhsceewz79+93dt2IvJa5DcHLftkg9ngREXm+dgWv2NhYvPbaa7hy5QrefvttXL16FZMmTcKwYcPw6quv4vr1686uJ5FXae0lgwCuXk9E5E3uanK9Wq3Gww8/jO3bt+OVV17BuXPnsHLlSkRGRmL+/Pm4evVqq55n48aNiIqKgk6ng8FguGPv2fbt2zFo0CDodDoMHz4cu3btctgvhEBKSgrCw8Ph6+sLo9GIs2fPOpQpKSnBvHnzEBAQgKCgICxevBgVFRXS/jNnzuDee++FXq+HTqdD37598fzzz6O2traVPx2i5pW3ZahRxdXriYi8xV0Fr4MHD+KXv/wlwsPD8eqrr2LlypU4f/48MjMzceXKFTz44IN3fI5t27YhOTkZa9aswaFDhzBy5EjEx8fj2rVrTZbft28f5s6di8WLF+Pw4cNISEhAQkICTpw4IZVZu3YtNmzYgLS0NOTk5MDPzw/x8fEO89HmzZuHkydPIjMzEzt37sTevXuxdOlSab+Pjw/mz5+Pzz77DGfOnMH69evxt7/9DWvWrLmLnxiRjbmV12oE2ONFRORNFEKINi+H/eqrr2LTpk04c+YMZs6ciV/84heYOXMmlMqGHHfp0iVERUWhrq7lPxYGgwHjxo3D66+/DgCwWq2IjIzEU089hVWrVjUqn5iYCLPZjJ07d0rbJkyYgNjYWKSlpUEIgYiICKxYsQIrV64EAJSVlUGv1yM9PR1z5szB6dOnMWTIEBw4cABjx44FAGRkZGDmzJm4dOkSIiIimqxrcnIyDhw4gC+//LJVPyeTyYTAwECUlZUhICCgVY+hziEuNQtXy6rwcdI9GNErCFtyCpot+8GhSziYfxO/ih+IJ+/t78ZaEhF1Tq78+92uHq833ngDjz76KPLz87Fjxw7cf//9DqELAEJDQ/H3v/+9xeepqalBbm4ujEZjQ4WUShiNRmRnZzf5mOzsbIfyABAfHy+Vz8vLQ2FhoUOZwMBAGAwGqUx2djaCgoKk0AUARqMRSqUSOTk5Tb7uuXPnkJGRgalTpzbbnurqaphMJocbUVPadlYj1/EiIvIW7QpemZmZePbZZxEeHu6wXQiBggLbf+4ajQYLFixo8XmKi4thsVig1+sdtuv1ehQWFjb5mMLCwhbL2+/vVCY0NNRhv1qtRnBwcKPXnThxInQ6HWJiYjB58mS8+OKLzbYnNTUVgYGB0i0yMrLZstR5CSFafckgoCF48axGIiLP167g1a9fPxQXFzfaXlJSgujo6LuuVEeybds2HDp0CFu2bMEnn3yCP/3pT82WXb16NcrKyqTbxYsX3VhT8hRVtVbYr3fdquUkVOzxIiLyFnf+rd+E5qaFVVRUQKfTtfp5QkJCoFKpUFRU5LC9qKgIYWFhTT4mLCysxfL2+6KiIoceuaKiIsTGxkplfjh5v66uDiUlJY1e195rNWTIEFgsFixduhQrVqyAStV4UrRWq4VWq71Ts6mTq7gtQHXxaf3kevZ4ERF5vjYFr+TkZAC268elpKSgS5cu0j6LxYKcnBwp3LSGRqPBmDFjkJWVhYSEBAC2yfVZWVlISkpq8jFxcXHIysrC8uXLpW2ZmZmIi4sDAERHRyMsLAxZWVlSXUwmE3JycvDEE09Iz1FaWorc3FyMGTMGALB7925YrVYYDIZm62u1WlFbWwur1dpk8CJqjduHGZX2K2C3QFq5nmc1EhF5vDYFr8OHDwOw9XgdP34cGo1G2qfRaDBy5EjpTMLWSk5OxoIFCzB27FiMHz8e69evh9lsxqJFiwAA8+fPR8+ePZGamgoAWLZsGaZOnYp169Zh1qxZ2Lp1Kw4ePIg333wTgC0ULl++HC+99BJiYmIQHR2NF154AREREVK4Gzx4MKZPn44lS5YgLS0NtbW1SEpKwpw5c6QzGjdv3gwfHx8MHz4cWq0WBw8exOrVq5GYmAgfH582tZHodhVtWEoCuK3Hi+t4ERF5vDYFr88//xwAsGjRIrz22mtOOcUyMTER169fR0pKCgoLCxEbG4uMjAxpcnxBQYHDGZMTJ07Eli1b8Pzzz+O5555DTEwMduzYgWHDhkllnnnmGZjNZixduhSlpaWYNGkSMjIyHIZBN2/ejKSkJEybNg1KpRKzZ8/Ghg0bpP1qtRqvvPIKvvvuOwgh0KdPHyQlJeG///u/77rN1Lm15XJBQMMlg9jjRUTk+dq1jhe1Dtfxoqbs/rYIP08/iBG9AvFx0iQAaHEdrwvFZrz55feIDvHD5yt/5KZaEhF1Xq78+93qHq+HH34Y6enpCAgIwMMPP9xi2Q8++OCuK0bkrSrqhwz9NK37+HEdLyIi79Hq4BUYGAiFQiF9TUTt096hRp7VSETk+VodvDZt2tTk10TUNhVV9rMa2za53lxTByGE9A8QERF5nnYtoHrr1i1UVlZK3+fn52P9+vX47LPPnFYxIm/VlssFAQ3BSwjb4qtEROS52hW8HnzwQbzzzjsAgNLSUowfPx7r1q3Dgw8+iDfeeMOpFSTyNm25XBAA+KgaPqY8s5GIyLO1K3gdOnQIkydPBgD885//RFhYGPLz8/HOO+84LMlARI3Zw1Nre7yUCgW6aGzDklzLi4jIs7UreFVWVqJr164AgM8++wwPP/wwlEolJkyYgPz8fKdWkMjbSGc1tjJ4AUCX+jMg2eNFROTZ2hW8+vfvjx07duDixYv497//jfvuuw8AcO3aNa5XRXQHDUONrb/slH2V+0oGLyIij9au4JWSkoKVK1ciKioKBoNBuk7iZ599hlGjRjm1gkTepq2T64Hberw41EhE5NHadMkgu0ceeQSTJk3C1atXMXLkSGn7tGnT8NBDDzmtckTeqK2T6wHAT8MeLyIib9Cu4AUAYWFhCAsLc9g2fvz4u64QkbdrT/DqomWPFxGRN2hX8DKbzXj55ZeRlZWFa9euwWp1XFvo+++/d0rliLxReybXs8eLiMg7tCt4/eIXv8AXX3yBn/3sZwgPD+dK2kRt0K4eL+msRvZ4ERF5snYFr08//RSffPIJ7rnnHmfXh8irWawCt2rb0eNlP6uRF8omIvJo7TqrsVu3bggODnZ2XYi83u3rcPm1YTkJ9ngREXmHdgWv3/3ud0hJSXG4XiMR3Zn9Atk+KgW06jas48U5XkREXqFdQ43r1q3D+fPnodfrERUVBR8fH4f9hw4dckrliLyNuR1reAE8q5GIyFu0K3glJCQ4uRpEnYO0eKqmbR899ngREXmHdgWvNWvWOLseRJ2CvceqLWc0AuzxIiLyFu2a4wUApaWleOutt7B69WqUlJQAsA0xXr582WmVI/I2DZcLav38LoA9XkRE3qJdPV7Hjh2D0WhEYGAgLly4gCVLliA4OBgffPABCgoK8M477zi7nkReod1zvHhWIxGRV2hXj1dycjIWLlyIs2fPQqfTSdtnzpyJvXv3Oq1yRN7GvpxEV10b53hxHS8iIq/QruB14MABPPbYY4229+zZE4WFhXddKSJv1d7J9ezxIiLyDu0KXlqtFiaTqdH27777Dj169LjrShF5q/YPNXKOFxGRN2hX8HrggQfw4osvora2FgCgUChQUFCAZ599FrNnz3ZqBYm8SXvParT3kNVaBGrqrHcoTUREHVW7gte6detQUVGBHj164NatW5g6dSr69++Prl274ve//72z60jkNSra2ePlq2k4C5K9XkREnqtdZzUGBgYiMzMTX3/9NY4ePYqKigqMHj0aRqPR2fUj8ir2oUb/Ni4noVEroVEpUWOxwlxjQVAXV9SOiIhcrc3By2q1Ij09HR988AEuXLgAhUKB6OhohIWFQQgBhULhinoSeYX29ngBQBetCjWVVp7ZSETkwdo01CiEwAMPPIBf/OIXuHz5MoYPH46hQ4ciPz8fCxcuxEMPPeSqehJ5hbsJXn48s5GIyOO16bd/eno69u7di6ysLNx7770O+3bv3o2EhAS88847mD9/vlMrSeQtGoYa29HjpeFaXkREnq5NPV7vvfcennvuuUahCwB+/OMfY9WqVdi8ebPTKkfkbexnNbZvqJE9XkREnq5NwevYsWOYPn16s/tnzJiBo0eP3nWliLxVRTsn1wO8XiMRkTdoU/AqKSmBXq9vdr9er8fNmzfvulJE3kgIIQWvrjqfNj9eWr2+mj1eRESeqk3By2KxQK1ufohEpVKhro7/jRM1pbLGAotVAAAC2hG8pOs1sseLiMhjtWmiiRACCxcuhFarbXJ/dXW1UypF5I1MVbYrPfioFND5tH3tYvZ4ERF5vjYFrwULFtyxDM9oJGqa6ZatpypA59Ou9e44x4uIyPO1KXht2rTJVfUg8nrl9T1eXXXtumCEdFZjJc9qJCLyWO26ViMRtZ19qDHAt+3zu4CGHi8ze7yIiDwWgxeRm9w+1NgeUo8X53gREXksBi8iN2no8WrfUCN7vIiIPB+DF5GblFfVr+GlbWePl4ZzvIiIPB2DF5GbmG7dZY9X/TpeZl6rkYjIYzF4EbmJNNTY3jle7PEiIvJ4DF5EbmKfXN/e5SS4cj0Rkedj8CJyk7tfToIr1xMReToGLyI3MVXd5XIS9Wc13qptuOYjERF5FgYvIjcpv3V3PV7+tw1RVlRxuJGIyBMxeBG5iekuLxmkVaugUSsdnouIiDwLgxeRm0hDje3s8QKAgPrQVs4eLyIij8TgReQGVbUW1NRZATSEp/boWj8/rJw9XkREHonBi8gN7EODSkXD2Ynt0ZU9XkREHo3Bi8gN7Gt4+WvVUCoV7X4ee/Cq4Or1REQeicGLyA3K73INLzv7dR451EhE5JkYvIjc4G7X8LKz93iZONRIROSR2j/ZhIha7dPjVwHYFj/dklPQ7udpmFzP4EVE5InY40XkBrdqbZf50fmo7up5GibXc6iRiMgTdYjgtXHjRkRFRUGn08FgMGD//v0tlt++fTsGDRoEnU6H4cOHY9euXQ77hRBISUlBeHg4fH19YTQacfbsWYcyJSUlmDdvHgICAhAUFITFixejoqJC2r9nzx48+OCDCA8Ph5+fH2JjY7F582bnNZo6lepa21ISvj5395HjWY1ERJ5N9uC1bds2JCcnY82aNTh06BBGjhyJ+Ph4XLt2rcny+/btw9y5c7F48WIcPnwYCQkJSEhIwIkTJ6Qya9euxYYNG5CWloacnBz4+fkhPj4eVVVVUpl58+bh5MmTyMzMxM6dO7F3714sXbrU4XVGjBiBf/3rXzh27BgWLVqE+fPnY+fOna77YZDXYo8XEREBgEIIIevVdg0GA8aNG4fXX38dAGC1WhEZGYmnnnoKq1atalQ+MTERZrPZIQBNmDABsbGxSEtLgxACERERWLFiBVauXAkAKCsrg16vR3p6OubMmYPTp09jyJAhOHDgAMaOHQsAyMjIwMyZM3Hp0iVEREQ0WddZs2ZBr9fj7bffbnJ/dXU1qqurpe9NJhMiIyNRVlaGgICA9v2AyCsk/jUbOXkl+PGgUBgH69v8+EcNvQEAu45fxS83H8LYPt3wzycmOruaREQE29/vwMBAl/z9lrXHq6amBrm5uTAajdI2pVIJo9GI7OzsJh+TnZ3tUB4A4uPjpfJ5eXkoLCx0KBMYGAiDwSCVyc7ORlBQkBS6AMBoNEKpVCInJ6fZ+paVlSE4OLjZ/ampqQgMDJRukZGRLbSeOhPn93hxqJGIyBPJGryKi4thsVig1zv2AOj1ehQWFjb5mMLCwhbL2+/vVCY0NNRhv1qtRnBwcLOv+/777+PAgQNYtGhRs+1ZvXo1ysrKpNvFixebLUudi/PmeHEdLyIiT8blJFrh888/x6JFi/C3v/0NQ4cObbacVquFVqt1Y83IUzi9x4sr1xMReSRZe7xCQkKgUqlQVFTksL2oqAhhYWFNPiYsLKzF8vb7O5X54eT9uro6lJSUNHrdL774Aj/96U/x5z//GfPnz29jC4lsqpwcvCqq62C1yjo9k4iI2kHW4KXRaDBmzBhkZWVJ26xWK7KyshAXF9fkY+Li4hzKA0BmZqZUPjo6GmFhYQ5lTCYTcnJypDJxcXEoLS1Fbm6uVGb37t2wWq0wGAzStj179mDWrFl45ZVXHM54JGorZwUv+8r3QgDmGvZ6ERF5GtmHGpOTk7FgwQKMHTsW48ePx/r162E2m6W5VPPnz0fPnj2RmpoKAFi2bBmmTp2KdevWYdasWdi6dSsOHjyIN998EwCgUCiwfPlyvPTSS4iJiUF0dDReeOEFREREICEhAQAwePBgTJ8+HUuWLEFaWhpqa2uRlJSEOXPmSGc0fv7557j//vuxbNkyzJ49W5r7pdFoWpxgT9SUKmmO190FL61aCR+VArUWgfKqOmnOFxEReQbZg1diYiKuX7+OlJQUFBYWIjY2FhkZGdLk+IKCAiiVDR1zEydOxJYtW/D888/jueeeQ0xMDHbs2IFhw4ZJZZ555hmYzWYsXboUpaWlmDRpEjIyMqDT6aQymzdvRlJSEqZNmwalUonZs2djw4YN0v5//OMfqKysRGpqqhT6AGDq1KnYs2ePC38i5G1qLVbUWGzBS3eXk+sVCgW66nxQYq7hmY1ERB5I9nW8vJkr1wEhz1FirsHo32UCAH734DColIo2P4d9HS8AmPrHz5F/oxL/fDwOY6PY+0pE5Gxeu44XUWdgumVb+kGjVrYrdP2Qv5ZreREReSoGLyIXM9WvuaVTO+fjZj+z0cS1vIiIPA6DF5GL3TDXAAD8tM6ZUtmwiCp7vIiIPA2DF5GL3aiwBS9/pwWvhrW8iIjIszB4EbnYjQrbhdOd1eMVwMsGERF5LAYvIhezDzU6u8eLQ41ERJ6HwYvIxYqd3OPF4EVE5LkYvIhcrGGO192tWm/XlUONREQei8GLyMVKnH5Wo305CfZ4ERF5GgYvIhezT6533hwvLidBROSpGLyIXEgIgWIX9XhxqJGIyPMweBG5UEV1HWrqbBfI9tM4KXjxkkFERB6LwYvIhewT6zVqJTROu2SQbaixoroOvMY9EZFnYfAicqEbZufO7wIahhotVoFbtRanPS8REbkegxeRCxXX93j5aZyzlAQAdNGooFIqAHC4kYjI0zB4EbmQs6/TCAAKhUJ6Pk6wJyLyLAxeRC7k7Os02nEtLyIiz8TgReRCzr5Oo539Qtllt9jjRUTkSRi8iFzI2ddptOvurwEAlNQPZRIRkWdg8CJyIVfM8QKAEH+t7fnrz5okIiLPwOBF5EL2YOT0Hi8/W4/XDfZ4ERF5FAYvIhdyVY9X9/oer2IGLyIij8LgReQiFqtASaX9Oo3OW8cLaJjjxaFGIiLPwuBF5CI3K2sgBKBQAF2cdJ1GuxB/DjUSEXkiBi8iF7GHom5dNNJK887S3a9+cn0Fe7yIiDwJgxeRi9hDkX0ivDPZhxqLK2p4oWwiIg/C4EXkIvbFU+0hyZnsPV41FivKq7l6PRGRp2DwInIRqcer/gxEZ/LVqKQLb3OeFxGR52DwInKRq6YqAEAPFwQvoCHQcZ4XEZHnYPAicpFLN28BACKDu7jk+W+f50VERJ6BwYvIRezBq1c3X5c8v3RmI9fyIiLyGAxeRC5y+WYlANcFL67lRUTkeRi8iFzgVo1FGgLs1c21Q42c40VE5DkYvIhc4HKprberq06NQF8fl7yGfaix2MweLyIiT8HgReQCF6X5Xa7p7QLY40VE5IkYvIhcwNUT6wEgRFpOgj1eRESegsGLyAUuuXhiPXBbjxeHGomIPAaDF5ELXHLHUGP9HK+blTWos1hd9jpEROQ8DF5ELuCOocZuXXygUABCADcra132OkRE5DxquStA5I2cvYbXlpyCJrf7+qhQWWPB/2bnI/m+AU55LSIich32eBE5mTvW8LLz19r+d6qornPp6xARkXMweBE52eVS2zCjK9fwsvOrD15mBi8iIo/A4EXkZA1nNLq2twtgjxcRkadh8CJyMndMrLfzY/AiIvIoDF5ETubO4NWti20os4RreREReQQGLyInc+dQY3c/2yKqDF5ERJ6BwYvIyb6/bgYA9A52ffAKrl9E9YaZ12skIvIEDF5ETlRnseLc9QoAwEB9V5e/XnB9j1dVrRWllez1IiLq6Bi8iJzowg0zauqs6KJRuWWOl0atRFedbYJ9/o1Kl78eERHdHQYvIif6trAcADBA3xVKpcItr2nv9bpww+yW1yMiovZj8CJyojP1wWtQmOuHGe3sF8suYI8XEVGHx+BF5ET2Hq+BbgxeDT1eDF5ERB0dgxeRE52RIXh197cFr4ISDjUSEXV0DF5ETmKurkNBia3XaVBYgNte176WFyfXExF1fAxeRE7yXZGtt6tHV600/OcO9jle18qrUVnDSwcREXVksgevjRs3IioqCjqdDgaDAfv372+x/Pbt2zFo0CDodDoMHz4cu3btctgvhEBKSgrCw8Ph6+sLo9GIs2fPOpQpKSnBvHnzEBAQgKCgICxevBgVFRXS/qqqKixcuBDDhw+HWq1GQkKC09pL3mdLTgG25BTgnex8AECgr4+0zX5zJV+NCr4+KgCQetyIiKhjkjV4bdu2DcnJyVizZg0OHTqEkSNHIj4+HteuXWuy/L59+zB37lwsXrwYhw8fRkJCAhISEnDixAmpzNq1a7FhwwakpaUhJycHfn5+iI+PR1VVlVRm3rx5OHnyJDIzM7Fz507s3bsXS5culfZbLBb4+vri6aefhtFodN0PgLxKocl2jIUF6Nz+2vZ5XheKGbyIiDoyhRBCyPXiBoMB48aNw+uvvw4AsFqtiIyMxFNPPYVVq1Y1Kp+YmAiz2YydO3dK2yZMmIDY2FikpaVBCIGIiAisWLECK1euBACUlZVBr9cjPT0dc+bMwenTpzFkyBAcOHAAY8eOBQBkZGRg5syZuHTpEiIiIhxec+HChSgtLcWOHTva3D6TyYTAwECUlZUhIMB9c37Ivew9Wm99+T2+LzZj9uheGNOnm1vrsPVAAY5dKsNzMwdh6ZR+bn1tIiJv48q/37L1eNXU1CA3N9ehR0mpVMJoNCI7O7vJx2RnZzfqgYqPj5fK5+XlobCw0KFMYGAgDAaDVCY7OxtBQUFS6AIAo9EIpVKJnJycu2pTdXU1TCaTw406ByGErD1ewZxgT0TkEWQLXsXFxbBYLNDr9Q7b9Xo9CgsLm3xMYWFhi+Xt93cqExoa6rBfrVYjODi42ddtrdTUVAQGBkq3yMjIu3o+8hw3K2tRWWOBSqFAaIDW7a9vn2DP4EVE1LHJPrnem6xevRplZWXS7eLFi3JXidwkv/5yPRFBOvio3P+xCu1qC16nrpog4+wBIiK6A9mCV0hICFQqFYqKihy2FxUVISwsrMnHhIWFtVjefn+nMj+cvF9XV4eSkpJmX7e1tFotAgICHG7UOdhXjY/q7ifL64cH6qBRKVFirmGvFxFRByZb8NJoNBgzZgyysrKkbVarFVlZWYiLi2vyMXFxcQ7lASAzM1MqHx0djbCwMIcyJpMJOTk5Upm4uDiUlpYiNzdXKrN7925YrVYYDAantY86F3uPVx+ZgpdapcSwnragn5t/U5Y6EBHRnanlfPHk5GQsWLAAY8eOxfjx47F+/XqYzWYsWrQIADB//nz07NkTqampAIBly5Zh6tSpWLduHWbNmoWtW7fi4MGDePPNNwEACoUCy5cvx0svvYSYmBhER0fjhRdeQEREhLQW1+DBgzF9+nQsWbIEaWlpqK2tRVJSEubMmeNwRuOpU6dQU1ODkpISlJeX48iRIwCA2NhYt/18yDNUVtfhWnk1AKB39y6y1WNMn244VFCKQwU3MXtML9nqQUREzZM1eCUmJuL69etISUlBYWEhYmNjkZGRIU2OLygogFLZ0Ck3ceJEbNmyBc8//zyee+45xMTEYMeOHRg2bJhU5plnnoHZbMbSpUtRWlqKSZMmISMjAzpdw5lmmzdvRlJSEqZNmwalUonZs2djw4YNDnWbOXMm8vPzpe9HjRoFAJw/Q43k1y9a2sNfC3+tfB+p0b27AcjDoYJS2epAREQtk3UdL2/Hdbw6h/l/z8Hes8UY26cbHh4tX0/TtMGhMPwhC0oFcOw38bKGQCIiT+aV63gReQu5J9bb6QN06BnkC6sAjl0slbUuRETUNAYvortQVWvB5dJbAIA+Ms7vshvVOwgAJ9gTEXVUDF5Ed+HIxVJYrAJdtWpp9Xg52eZ5AYcKGLyIiDoiBi+iu/D5GduacP1C/aFQKGSuDTC6/hqRhy+Wwmrl9E0ioo6GwYvoLmSdtgWvQWFdZa6JzZDwAPhpVCitrMXRS6VyV4eIiH6AwYuonfJvmHHuWgWUCmCAvmMEL41aiWmDbcuxfHLsqsy1ISKiH2LwImqn/9T3dkWF+EHno5K5Ng1mjQgHAOw6fpXDjUREHQyDF1E7ZZ22XRN0cFjHWqNt6oAe8NOocKWsCkc43EhE1KEweBG1g6mqFvvzSgB0nPlddjofFX4yhMONREQdEYMXUTt8ceY66qwC/UP90d1fK3d1Gpk1wnbdUQ43EhF1LAxeRO1g70maNjhU5po0bXJMCLpq1bhaVoUDF0rkrg4REdXjxdyI2uimuQZZ39rmdz00qicO5ZfKW6F6W3IKHL4fGNYVB/Nv4sWdpzDP0AePGnrLVDMiIrJjjxdRG3189ApqLQLDegZgUAebWH+7e/qHAABOXTGhuKJa5toQERHA4EXUZv/MvQQAeGR0L5lr0jJ9gA4D9V0hAHx1tlju6hARERi8iNrkTGE5jl8ug49KgQdie8pdnTuaPMDW63Wo4CZ7vYiIOgAGL6I2+NchW2/XjweFdoiLYt9JdHc/9OrmizqrwN+/ypO7OkREnR6DF1ErVdVasP3gRQDA7A4+zGinUCjwowG2My/f/ioPV0pvyVwjIqLOjcGLqJX+degSblbWIjLYV7oeoicYHN4VUd39UF1nxR//fUbu6hARdWpcToLoDrbkFMAqBNb/5ywAYETPIGw7cFHmWrWeQqHAzOFh+J895/Hh4ctYdE8URvQKkrtaRESdEnu8iFrhu8JyFFdUQ+ejxNg+3eSuTpv16tYFD4+ynQzw4v+d4mr2REQyYfAiaoWvztmWYxgXFQytj0rm2rTPr6YPRBeNCgfzb2J7ruf02BEReRMGL6I7uFBsxvfFZigVQFzf7nJXp93CA32R/JMBAIA/7PqWy0sQEcmAwYuoBUII/PtkIQBgbJ9gBHXp+EtItGThxCgMCQ9A2a1a/P6T03JXh4io02HwImpB1ulryC+phI9KgR8P6pgXxG4LtUqJPzw8HAoF8OHhy1zRnojIzXhWI1EzLFaBtf/+FgAwsV8IAnx9ZK7R3bn9ItqG6O745vsbWLb1MJ6eFgMfle1/MF5Im4jItdjjRdSMd7/Jx3dFFdD5KDElpofc1XGq+4boEaBT44a5BnvOXJO7OkREnQaDF1ET8orNSP3UNgfqJ4P18NV45pmMzdH5qHD/iAgAwN7vilFkqpK5RkREnQODF9EPWKwCK94/gqpaKyb26w6DB5/J2JKhEQEYFNYVFiHw4eHLsAqu7UVE5GoMXkQ/8OfM73CooBT+WjXWPjICSoVC7iq5hEKhwAMjI6BRK1FQUolvvr8hd5WIiLwegxfRbd768nu8/vk5AMBvHxiKXt26yFwj1wrqosH0oWEAgM9OFuHSzUqZa0RE5N0YvIjqbc7Jx0v1a1utvG8AZo/pJXON3GN8dDCiundBjcWKZ/91jJcTIiJyIQYv6vQsVoHff3IKv/7wBADgsSl98eS9/WWulfsoFQo8PLoXfFQKfH3uBtL3XZC7SkREXovBizo1U1Utfp5+AH/7Mg8A8NSP+2PVjEFQeOm8ruaE+GsxY1g4AODljG9xtqhc5hoREXknLqBKnVZesRn/Ly0bxRXV8FEpMHt0L4QH+uK9/Z3zAtKG6GCU3arFF99dx1PvHcaHv7zH65bRICKSG3u8qFM6cKEED77+FYorqhHo64Olk/thRK8guaslK4VCgT8+MgLd/TT4trAcz/7rGASXmCAicioGL+p0vjpbjPl/3w9TVR0iu/nilz/qh57dfOWuVocQGqDDxnmjoVIq8PHRK3irfgiWiIicQyH4L63LmEwmBAYGoqysDAEBAXJXhwDsOXMNS/83FzV1Vkwd0AM/HhQqXaeQGmSfL8b/HbsKBYBHxvTCqN7dAPBajkTUObjy7zf/4lCncfRiKZ549xBq6qz4yRA93pw/hqGrGRP6dsf46GAIAP/MvYTc/JtyV4mIyCtwcj15tS05BQCAGxXVSPviPG7VWhAT6o8pMT3wr9zLMteu47Kvag8A+/NK8MGhSygxV+ORMb2gUTOsEhG1F3+DkterqK5D+r4LMNdYEB6ow6Pje0Ol7FzLRbSHUqHAgyMjENevOwSAz89cR8LGr5F9/gYn3RMRtRN7vMir1dRZ8b/ZF3DDXIMgXx8smBgFrQ+XSGgthUKBn46IQFR3P3x05DJOXTVh7t++Qb8efnh4dC9MjgnB0IhABlkiolbi5HoX4uR6eVmsAvf/5SucvmqCr48Kj03pi9AAndzV8ljlVbW4cKMSHx25jMoai7S9u58GPx0ZgUfG9MKwnoEy1pCIyDlc+febwcuFGLzkI4RAykcn8b/f5EOtVODn90QjKsRP7mp5vEcNvVFeVYudx65i97fX8M35GyivrpP23z8iHL97cBi6+WlkrCUR0d1h8PJQDF7yeWPPebyS8S0UAOaM743h7IlxCYtV4Ny1ChwquImTV8pgFbbLD637r5GYOqCH3NUjImoXLidB1AbbDhTglYxvAQAzh4czdLmQSqnAwLCumDu+Nx6f2g+hXbUorqjG4vQD+PT4VbmrR0TU4TB4kVfZnJOPZ/91HADwi0nRuKd/iMw16jx6deuCJ+/tjwdGRqDOKpD03mF8fPSK3NUiIupQGLzIKwghkPbFefz6wxMAgEX3ROHXswbLXKvOx0elxJ8TYzF7dC9YrAL/ve0IPjtZKHe1iIg6DC4nQR7PVFWLZ7YfQ0b9H/jHpvTFqhmDoFBwiQM5bDtwEaN6ByGv2IxDBTfxxOZDWDgxCv16+APgZYeIqHNjjxd5LCEEMk5cxYz1XyLjZCF8VAr87sGhDF0dgFKhwEOjemJoRAAsVoH/zc7HxZJKuatFRCQ7Bi/yOEII7DtfjHlv5eDxdw/hcukt9AzyxfbHJ+JncVEMXR2ESqlA4thI9O/hjxqLFen7LqDQVCV3tYiIZMXlJFyIy0k4V0V1HXYdu4r//SYfxy+XAQDUSgUmx4Rg6oBQXkOwg6qus+Dtr/Jw8eYtdNWp8X9Jk7imGhF1aFzHy0MxeN09IQSOXirDlpx87Dx2VVoxXeejxMheQZgS04OLdXqAypo6/O3L71FkqkaIvwZ/XzAOIyOD5K4WEVGTGLw8FINX+5mqavHR4cvYsv8iTl81Sdv7hvjhkbG9MGdcb2Sc4NlynqS8qhbp+y7galkVfH1UWD8nFvFDw+SuFhFRIwxeHorBq22EEDhysRTv7S/A/x29ilu1tt4ttVKB4T0DMTYqGFHdu3AOlwerrrVg95lr+PJsMQBg4cQorJoxCDpeuJyIOhBX/v3mchIku+Z6t3p01WJ8VDBG9Q5CFw0PVW+g9VHh7YXjkLrrW7z9dR7S911A9vkbeOmhYRgXFSx39YiIXI49Xi7EHq/mWa0Cr2R8i4P5N3HsUilqLbbD0N67NS4qGH3Yu+WV7Ot4ff7tNazcfhQ3zDUAgITYCCwzDkA0J94Tkcy8/lqNGzduRFRUFHQ6HQwGA/bv399i+e3bt2PQoEHQ6XQYPnw4du3a5bBfCIGUlBSEh4fD19cXRqMRZ8+edShTUlKCefPmISAgAEFBQVi8eDEqKiocyhw7dgyTJ0+GTqdDZGQk1q5d65wGd0JCCFwuvYX/O3oFz+84jriXs/DXvd8jN/8mai0CoV21uH9EOFbNGIT/NzYSUSF+DF1eaktOAbbkFOBqWRUen9oP46KCoQCw48gV/PhPe/DEu7nY/W0RquqHmomIvInsPV7btm3D/PnzkZaWBoPBgPXr12P79u04c+YMQkNDG5Xft28fpkyZgtTUVNx///3YsmULXnnlFRw6dAjDhg0DALzyyitITU3FP/7xD0RHR+OFF17A8ePHcerUKeh0OgDAjBkzcPXqVfz1r39FbW0tFi1ahHHjxmHLli0AbGl3wIABMBqNWL16NY4fP46f//znWL9+PZYuXdqqtnWmHi8hBExVdSgx1+BK6S1culmJyzdv4VLpLeTfqMTZonKYquocHqNVKzEkPADjo4PRO5i9W53ZpZuVyDp9DWeKyqVtvj4qjOgViOgQP4QF6qBVq+CjUqDWIlBrsaLOYkWNRUCtVKCLVgU/jRpdNCr4a9UI9tOgu78WIf4aBOh8oFTy2CKi1vPqyfUGgwHjxo3D66+/DgCwWq2IjIzEU089hVWrVjUqn5iYCLPZjJ07d0rbJkyYgNjYWKSlpUEIgYiICKxYsQIrV64EAJSVlUGv1yM9PR1z5szB6dOnMWTIEBw4cABjx44FAGRkZGDmzJm4dOkSIiIi8MYbb+DXv/41CgsLodHYlitYtWoVduzYgW+//bZVbXPVG/fvk4WoswhYhIDVKmAVAhbpHrBYbX+Qai1W1NZZUWu57fv6mxC2BS7VSgWUP7hXKZVQKRRQqxRQKRUQAjBX18FcU1d/b7HdV9fBdKsOJZU1uGmuQZ215UNJqQDCA33RO7gLBuj90a+HP9SqDtHpSh1EkakKNytrkHmqCFfLnLPYqlqpgL9ODY1KCY1aCa1aCZ/6487+2RGi/mshYLXavrbdIH3Gbv/aUv+9EAI+Kttz6nxU0KqV0KpV0Poopa91PvXb1Mr67aqG+/r6aH1U0NXfq+s/cwK2z5Pta9R/3fAZa1SmvpzVKlBrtaLOHlCtwvZ7wCpQZ//eYttvra+/Rq2Ej0oBH5XtZ6NR1X+vbvherVJA6cJ/jlwZjQVsPx/b1+K2rxv/TBuV+cHPXyrd7HvU+DXs39xeD6sQqKmz2m4W2311/c1qFfBRK6BRqervldJ746Oy/Z5Wq5Q/uFdA4dKfYusI2D5Tt/+DVHv7sVh/7NmP0TqL7dgUQtT/TbIdj/a2aVQNbbRttx2L9mNSrVTCODjU6X9LvHZyfU1NDXJzc7F69Wppm1KphNFoRHZ2dpOPyc7ORnJyssO2+Ph47NixAwCQl5eHwsJCGI1GaX9gYCAMBgOys7MxZ84cZGdnIygoSApdAGA0GqFUKpGTk4OHHnoI2dnZmDJlihS67K/zyiuv4ObNm+jWrVujulVXV6O6ulr6vqzMtsinyWRqVPZu/HLT16i1WJ36nM7io1YiUKdGUBcfBOo0COyiRrcuGoT4a9HdX+Pw4aipMqNGxrpSx9NVBXTtqsDPx+txzVSNIlMVSiprUFFlgcVqhcUqbP8YKG3/OKgUClgFbH+0LFbUWiyorrWisqYOFTW2r2sAlNxyXZ2rAVTcsRQRucrB541OPzPa/nfbFX1Tsgav4uJiWCwW6PV6h+16vb7ZXqXCwsImyxcWFkr77dtaKvPDYUy1Wo3g4GCHMtHR0Y2ew76vqeCVmpqK3/72t422R0ZGNtkWIiIiujv69a577vLycgQGBjr1OXmOvhOtXr3aoTfOarWipKQE3bt3d/n8JZPJhMjISFy8eNFr55N1hjYCnaOdnaGNQOdoZ2doI9A52tkZ2gi0rp1CCJSXlyMiIsLpry9r8AoJCYFKpUJRUZHD9qKiIoSFNb2idVhYWIvl7fdFRUUIDw93KBMbGyuVuXbtmsNz1NXVoaSkxOF5mnqd21/jh7RaLbRarcO2oKCgJsu6SkBAgFd/YIDO0Uagc7SzM7QR6Bzt7AxtBDpHOztDG4E7t9PZPV12ss5s1mg0GDNmDLKysqRtVqsVWVlZiIuLa/IxcXFxDuUBIDMzUyofHR2NsLAwhzImkwk5OTlSmbi4OJSWliI3N1cqs3v3blitVhgMBqnM3r17UVtb6/A6AwcObHKYkYiIiOhOZD+lLDk5GX/729/wj3/8A6dPn8YTTzwBs9mMRYsWAQDmz5/vMPl+2bJlyMjIwLp16/Dtt9/iN7/5DQ4ePIikpCQAgEKhwPLly/HSSy/h448/xvHjxzF//nxEREQgISEBADB48GBMnz4dS5Yswf79+/H1118jKSkJc+bMkboVH330UWg0GixevBgnT57Etm3b8NprrzWa2E9ERETUaqID+Mtf/iJ69+4tNBqNGD9+vPjmm2+kfVOnThULFixwKP/++++LAQMGCI1GI4YOHSo++eQTh/1Wq1W88MILQq/XC61WK6ZNmybOnDnjUObGjRti7ty5wt/fXwQEBIhFixaJ8vJyhzJHjx4VkyZNElqtVvTs2VO8/PLLzm24E1VVVYk1a9aIqqoquaviMp2hjUJ0jnZ2hjYK0Tna2RnaKETnaGdnaKMQ8rdT9nW8iIiIiDoL2YcaiYiIiDoLBi8iIiIiN2HwIiIiInITBi8iIiIiN2Hw6kB+//vfY+LEiejSpUuzC68WFBRg1qxZ6NKlC0JDQ/GrX/0KdXV1DmX27NmD0aNHQ6vVon///khPT2/0PBs3bkRUVBR0Oh0MBgP279/vsL+qqgpPPvkkunfvDn9/f8yePbvRgrLOEhUVBYVC4XB7+eWXHcocO3YMkydPhk6nQ2RkJNauXdvoebZv345BgwZBp9Nh+PDh2LVrl8N+IQRSUlIQHh4OX19fGI1GnD171iVtaq87vS8dyW9+85tG79ugQYOk/a05hpx1PDvL3r178dOf/hQRERFQKBTSNWDtWnMMlZSUYN68eQgICEBQUBAWL16MigrHqzk643h2ZTsXLlzY6L2dPn26R7UzNTUV48aNQ9euXREaGoqEhAScOXPGoYw7j1FXfLZb08Yf/ehHjd7Lxx9/3GPaCABvvPEGRowYIS14GhcXh08//VTa73HvoyznUlKTUlJSxKuvviqSk5NFYGBgo/11dXVi2LBhwmg0isOHD4tdu3aJkJAQsXr1aqnM999/L7p06SKSk5PFqVOnxF/+8hehUqlERkaGVGbr1q1Co9GIt99+W5w8eVIsWbJEBAUFiaKiIqnM448/LiIjI0VWVpY4ePCgmDBhgpg4caJL2t2nTx/x4osviqtXr0q3iooKaX9ZWZnQ6/Vi3rx54sSJE+K9994Tvr6+4q9//atU5uuvvxYqlUqsXbtWnDp1Sjz//PPCx8dHHD9+XCrz8ssvi8DAQLFjxw5x9OhR8cADD4jo6Ghx69Ytl7SrrVrzvnQka9asEUOHDnV4365fvy7tv9Mx5Kzj2Zl27dolfv3rX4sPPvhAABAffvihw/7WHEPTp08XI0eOFN9884348ssvRf/+/cXcuXOl/c46nl3ZzgULFojp06c7vLclJSUOZTp6O+Pj48WmTZvEiRMnxJEjR8TMmTNF7969HX63uOsYddVnuzVtnDp1qliyZInDe1lWVuYxbRRCiI8//lh88skn4rvvvhNnzpwRzz33nPDx8REnTpwQQnje+8jg1QFt2rSpyeC1a9cuoVQqRWFhobTtjTfeEAEBAaK6uloIIcQzzzwjhg4d6vC4xMREER8fL30/fvx48eSTT0rfWywWERERIVJTU4UQQpSWlgofHx+xfft2qczp06cFAJGdne2UNt6uT58+4s9//nOz+//nf/5HdOvWTWqjEEI8++yzYuDAgdL3//Vf/yVmzZrl8DiDwSAee+wxIYRtbbewsDDxxz/+UdpfWloqtFqteO+995zUkrtzp/elo1mzZo0YOXJkk/tacww563h2lR8GktYcQ6dOnRIAxIEDB6Qyn376qVAoFOLy5ctCCOccz87UXPB68MEHm32MJ7bz2rVrAoD44osvhBDuPUbd9dn+YRuFsAWvZcuWNfsYT2ujXbdu3cRbb73lke8jhxo9SHZ2NoYPHw69Xi9ti4+Ph8lkwsmTJ6UyRqPR4XHx8fHIzs4GANTU1CA3N9ehjFKphNFolMrk5uaitrbWocygQYPQu3dvqYyzvfzyy+jevTtGjRqFP/7xjw5dwNnZ2ZgyZQo0Go1Dm86cOYObN29KZVpqd15eHgoLCx3KBAYGwmAwuKxNbdGa96UjOnv2LCIiItC3b1/MmzcPBQUFAFp3DDnjeHan1hxD2dnZCAoKwtixY6UyRqMRSqUSOTk5Upm7PZ7dYc+ePQgNDcXAgQPxxBNP4MaNG9I+T2xnWVkZACA4OBiA+45Rd362f9hGu82bNyMkJATDhg3D6tWrUVlZKe3ztDZaLBZs3boVZrMZcXFxHvk+ynqRbGqbwsJChwMHgPR9YWFhi2VMJhNu3bqFmzdvwmKxNFnm22+/lZ5Do9E0mmem1+ul13Gmp59+GqNHj0ZwcDD27duH1atX4+rVq3j11Vel+kRHRzeqi31ft27dmm337T+X2x/n6ja1VXFx8R3fl47GYDAgPT0dAwcOxNWrV/Hb3/4WkydPxokTJ1p1DDnjePb19XVR6xprzTFUWFiI0NBQh/1qtRrBwcEOZe72eHa16dOn4+GHH0Z0dDTOnz+P5557DjNmzEB2djZUKpXHtdNqtWL58uW45557MGzYMKkO7jhGW/M711VtBGyXv+vTpw8iIiJw7NgxPPvsszhz5gw++OADj2rj8ePHERcXh6qqKvj7++PDDz/EkCFDcOTIEY97Hxm8XGzVqlV45ZVXWixz+vRph0nJ3qAt7b79+pcjRoyARqPBY489htTUVGi1WldXldppxowZ0tcjRoyAwWBAnz598P7777s1EJHzzZkzR/p6+PDhGDFiBPr164c9e/Zg2rRpMtasfZ588kmcOHECX331ldxVcZnm2rh06VLp6+HDhyM8PBzTpk3D+fPn0a9fP3dXs90GDhyII0eOoKysDP/85z+xYMECfPHFF3JXq10YvFxsxYoVWLhwYYtl+vbt26rnCgsLa3QGhf3MjbCwMOn+h2dzFBUVISAgAL6+vlCpVFCpVE2Wuf05ampqUFpa6vBfxO1l7uRu2m0wGFBXV4cLFy5g4MCBzbbJXlf7/Z3aZN8WHh7uUCY2NrZVbXKlkJCQO74vHV1QUBAGDBiAc+fO4Sc/+ckdjyFnHM/u1JpjKCwsDNeuXXN4XF1dHUpKSu7Ypttf407Hs7v17dsXISEhOHfuHKZNm+ZR7UxKSsLOnTuxd+9e9OrVS9remt9z7vqd66o2NsVgMAAAzp07h379+nlMGzUaDfr37w8AGDNmDA4cOIDXXnsNiYmJHvc+co6Xi/Xo0QODBg1q8Xb7HIiWxMXF4fjx4w6/8DIzMxEQEIAhQ4ZIZbKyshwel5mZibi4OAC2g3fMmDEOZaxWK7KysqQyY8aMgY+Pj0OZM2fOoKCgQCrjynYfOXIESqVSGsqIi4vD3r17UVtb69CmgQMHolu3bq1qd3R0NMLCwhzKmEwm5OTktLpNrtSa96Wjq6iowPnz5xEeHt6qY8gZx7M7teYYiouLQ2lpKXJzc6Uyu3fvhtVqlf7gOeN4drdLly7hxo0bUuD0hHYKIZCUlIQPP/wQu3fvbjTs6a5j1JWf7Tu1sSlHjhwBAIf3siO3sTlWqxXV1dWe+T62aSo+uVR+fr44fPiw+O1vfyv8/f3F4cOHxeHDh0V5ebkQouGU2Pvuu08cOXJEZGRkiB49ejR5SuyvfvUrcfr0abFx48YmT4nVarUiPT1dnDp1SixdulQEBQU5nPHx+OOPi969e4vdu3eLgwcPiri4OBEXF+f0Nu/bt0/8+c9/FkeOHBHnz58X7777rujRo4eYP3++VKa0tFTo9Xrxs5/9TJw4cUJs3bpVdOnSpdFp6Wq1WvzpT38Sp0+fFmvWrGlyOYmgoCDx0UcfiWPHjokHH3ywwy0ncaf3pSNZsWKF2LNnj8jLyxNff/21MBqNIiQkRFy7dk0IcedjyFnHszOVl5dLnzsA4tVXXxWHDx8W+fn5QojWHUPTp08Xo0aNEjk5OeKrr74SMTExDsssOOt4dlU7y8vLxcqVK0V2drbIy8sT//nPf8To0aNFTEyMqKqq8ph2PvHEEyIwMFDs2bPHYSmFyspKqYy7jlFXfbbv1MZz586JF198URw8eFDk5eWJjz76SPTt21dMmTLFY9oohBCrVq0SX3zxhcjLyxPHjh0Tq1atEgqFQnz22WdCCM97Hxm8OpAFCxYIAI1un3/+uVTmwoULYsaMGcLX11eEhISIFStWiNraWofn+fzzz0VsbKzQaDSib9++YtOmTY1e6y9/+Yvo3bu30Gg0Yvz48eKbb75x2H/r1i3xy1/+UnTr1k106dJFPPTQQ+Lq1atOb3Nubq4wGAwiMDBQ6HQ6MXjwYPGHP/zB4Re8EEIcPXpUTJo0SWi1WtGzZ0/x8ssvN3qu999/XwwYMEBoNBoxdOhQ8cknnzjst1qt4oUXXhB6vV5otVoxbdo0cebMGae36W7c6X3pSBITE0V4eLjQaDSiZ8+eIjExUZw7d07a35pjyFnHs7N8/vnnTX4GFyxYIIRo3TF048YNMXfuXOHv7y8CAgLEokWLpH+e7JxxPLuqnZWVleK+++4TPXr0ED4+PqJPnz5iyZIljf64dPR2NtU+AA7HjzuPUVd8tu/UxoKCAjFlyhQRHBwstFqt6N+/v/jVr37lsI5XR2+jEEL8/Oc/F3369BEajUb06NFDTJs2TQpdQnje+6gQQoi29ZERERERUXtwjhcRERGRmzB4EREREbkJgxcRERGRmzB4EREREbkJgxcRERGRmzB4EREREbkJgxcRERGRmzB4EREREbkJgxcRURtERUVh/fr1cleDiDwUgxcRdVoLFy6EQqGAQqGARqNB//798eKLL6Kurq7Zxxw4cABLly51Yy2JyJuo5a4AEZGcpk+fjk2bNqG6uhq7du3Ck08+CR8fH6xevdqhXE1NDTQaDXr06CFTTYnIG7DHi4g6Na1Wi7CwMPTp0wdPPPEEjEYjPv74YyxcuBAJCQn4/e9/j4iICAwcOBBA46HG0tJSPPbYY9Dr9dDpdBg2bBh27twp7f/qq68wefJk+Pr6IjIyEk8//TTMZrO7m0lEHQR7vIiIbuPr64sbN24AALKyshAQEIDMzMwmy1qtVsyYMQPl5eV499130a9fP5w6dQoqlQoAcP78eUyfPh0vvfQS3n77bVy/fh1JSUlISkrCpk2b3NYmIuo4GLyIiAAIIZCVlYV///vfeOqpp3D9+nX4+fnhrbfegkajafIx//nPf7B//36cPn0aAwYMAAD07dtX2p+amop58+Zh+fLlAICYmBhs2LABU6dOxRtvvAGdTufydhFRx8KhRiLq1Hbu3Al/f3/odDrMmDEDiYmJ+M1vfgMAGD58eLOhCwCOHDmCXr16SaHrh44ePYr09HT4+/tLt/j4eFitVuTl5bmiOUTUwbHHi4g6tXvvvRdvvPEGNBoNIiIioFY3/Fr08/Nr8bG+vr4t7q+oqMBjjz2Gp59+utG+3r17t6/CROTRGLyIqFPz8/ND//792/XYESNG4NKlS/juu++a7PUaPXo0Tp061e7nJyLvw6FGIqJ2mjp1KqZMmYLZs2cjMzMTeXl5+PTTT5GRkQEAePbZZ7Fv3z4kJSXhyJEjOHv2LD766CMkJSXJXHMikguDFxHRXfjXv/6FcePGYe7cuRgyZAieeeYZWCwWALYesS+++ALfffcdJk+ejFGjRiElJQUREREy15qI5KIQQgi5K0FERETUGbDHi4iIiMhNGLyIiIiI3ITBi4iIiMhNGLyIiIiI3ITBi4iIiMhNGLyIiIiI3ITBi4iIiMhNGLyIiIiI3ITBi4iIiMhNGLyIiIiI3ITBi4iIiMhN/j/RBxXEU6lZ1gAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.scatter(y_test, y_pred, alpha = 0.5)\n", "plt.xlabel(\"y_test\")\n", "plt.ylabel(\"y_pred\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "id": "6fP7XmL96VCE", "outputId": "c2e76b5a-5bc5-4d61-f5a5-4f3d82192f5f" }, "execution_count": 55, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from sklearn import metrics" ], "metadata": { "id": "Bp9P9cSG6YR_" }, "execution_count": 56, "outputs": [] }, { "cell_type": "code", "source": [ "print('Mean Absolute ERROR:', metrics.mean_absolute_error(y_test, y_pred))\n", "print('Mean Square Error:', metrics.mean_squared_error(y_test, y_pred))\n", "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OV6YUFWO6bW5", "outputId": "580d6634-7b3e-4b95-b623-60888ba78290" }, "execution_count": 57, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mean Absolute ERROR: 1187.2347040835286\n", "Mean Square Error: 3925052.529427591\n", "RMSE: 1981.1745328031022\n" ] } ] }, { "cell_type": "code", "source": [ "2090.5509/(max(y)-min(y))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dRgFx0UN6c95", "outputId": "6d5d22b9-2530-409d-ec0d-54161598924e" }, "execution_count": 58, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.026887077025966846" ] }, "metadata": {}, "execution_count": 58 } ] }, { "cell_type": "code", "source": [ "metrics.r2_score(y_test, y_pred)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RpKnNeSA6i1z", "outputId": "bf1c9bdb-0781-468e-bb80-a4a433c9d15e" }, "execution_count": 59, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8117505754702412" ] }, "metadata": {}, "execution_count": 59 } ] }, { "cell_type": "markdown", "source": [ "### HyperParamter Tuning" ], "metadata": { "id": "xH84BzBk6phJ" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import RandomizedSearchCV" ], "metadata": { "id": "7Uf8x4lz6krL" }, "execution_count": 60, "outputs": [] }, { "cell_type": "code", "source": [ "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n", "# Number of features to consider at every split\n", "max_features = ['auto', 'sqrt']\n", "# Maximum number of levels in tree\n", "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n", "# Minimum number of samples required to split a node\n", "min_samples_split = [2, 5, 10, 15, 100]\n", "# Minimum number of samples required at each leaf node\n", "min_samples_leaf = [1, 2, 5, 10]" ], "metadata": { "id": "6qYNhXUp6wMa" }, "execution_count": 61, "outputs": [] }, { "cell_type": "code", "source": [ "random_grid = {'n_estimators': n_estimators,\n", " 'max_features': max_features,\n", " 'max_depth': max_depth,\n", " 'min_samples_split': min_samples_split,\n", " 'min_samples_leaf': min_samples_leaf}" ], "metadata": { "id": "8Mz6Z5dO6ybX" }, "execution_count": 62, "outputs": [] }, { "cell_type": "code", "source": [ "rf_random = RandomizedSearchCV(estimator = reg_rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42)" ], "metadata": { "id": "XR05V5q660jC" }, "execution_count": 63, "outputs": [] }, { "cell_type": "code", "source": [ "rf_random.fit(X_train,y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "mZ7J3sct6-uq", "outputId": "40ea3ced-9a32-4cd6-d47f-57954e598db6" }, "execution_count": 64, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n", "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 4.2s\n", "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 4.1s\n", "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 5.7s\n", "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 4.3s\n", "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time= 4.2s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 7.8s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 7.0s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 9.4s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 6.4s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time= 7.9s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 3.7s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 4.4s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 4.8s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 3.9s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time= 3.9s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 8.3s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 7.3s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 8.1s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 7.8s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time= 7.7s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 12.1s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 12.2s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 11.8s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 11.3s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time= 12.3s\n", "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 11.4s\n", "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 13.1s\n", "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 11.1s\n", "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 9.9s\n", "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time= 11.0s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 3.5s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 4.8s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 3.4s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 3.5s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time= 4.0s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 2.7s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 1.7s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 1.6s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 1.6s\n", "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time= 1.7s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 2.1s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 2.7s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 3.3s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 2.1s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 2.3s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 14.5s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 15.0s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 14.5s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 14.6s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time= 15.3s\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(),\n", " param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n", " 'max_features': ['auto', 'sqrt'],\n", " 'min_samples_leaf': [1, 2, 5, 10],\n", " 'min_samples_split': [2, 5, 10, 15,\n", " 100],\n", " 'n_estimators': [100, 200, 300, 400,\n", " 500, 600, 700, 800,\n", " 900, 1000, 1100,\n", " 1200]},\n", " random_state=42, scoring='neg_mean_squared_error',\n", " verbose=2)" ], "text/html": [ "
RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
              "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
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              "                                                              100],\n",
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              "                                                         900, 1000, 1100,\n",
              "                                                         1200]},\n",
              "                   random_state=42, scoring='neg_mean_squared_error',\n",
              "                   verbose=2)
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" ] }, "metadata": {}, "execution_count": 64 } ] }, { "cell_type": "code", "source": [ "rf_random.best_params_" ], "metadata": { "id": "tA_LTkOf7AYr", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b9df903a-8725-4d3b-9fb3-939c28d7f006" }, "execution_count": 65, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'n_estimators': 700,\n", " 'min_samples_split': 15,\n", " 'min_samples_leaf': 1,\n", " 'max_features': 'auto',\n", " 'max_depth': 20}" ] }, "metadata": {}, "execution_count": 65 } ] }, { "cell_type": "code", "source": [ "rf_best=RandomForestRegressor(max_depth= 20,\n", " max_features= 'auto',\n", " min_samples_leaf= 1,\n", " min_samples_split= 15,\n", " n_estimators= 700)" ], "metadata": { "id": "K4NvR2aJB7EZ" }, "execution_count": 66, "outputs": [] }, { "cell_type": "code", "source": [ "rf_best.fit(X_train,y_train)" ], "metadata": { "id": "CAnNo1BJCoxn", "colab": { "base_uri": "https://localhost:8080/", "height": 148 }, "outputId": "465a69c8-8841-49db-ea4a-9468620ca788" }, "execution_count": 67, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n", " warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "RandomForestRegressor(max_depth=20, max_features='auto', min_samples_split=15,\n", " n_estimators=700)" ], "text/html": [ "
RandomForestRegressor(max_depth=20, max_features='auto', min_samples_split=15,\n",
              "                      n_estimators=700)
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" ] }, "metadata": {}, "execution_count": 67 } ] }, { "cell_type": "code", "source": [ "y_prediction = rf_best.predict(X_test)" ], "metadata": { "id": "65JFsMZVDGxL" }, "execution_count": 68, "outputs": [] }, { "cell_type": "code", "source": [ "metrics.r2_score(y_test, y_prediction)" ], "metadata": { "id": "6FtJipk1DKSN", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d79b2e83-886a-456a-8500-79044b75b609" }, "execution_count": 69, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8319550259102328" ] }, "metadata": {}, "execution_count": 69 } ] }, { "cell_type": "markdown", "source": [ "## XGboost" ], "metadata": { "id": "CTmS2BEaD4YF" } }, { "cell_type": "code", "source": [ "import xgboost as xg" ], "metadata": { "id": "YSNQmzyRDRPP" }, "execution_count": 70, "outputs": [] }, { "cell_type": "code", "source": [ "xgb_r = xg.XGBRegressor(n_estimators = 10, seed = 123)" ], "metadata": { "id": "xYY231qMEJdN" }, "execution_count": 71, "outputs": [] }, { "cell_type": "code", "source": [ "xgb_r.fit(X_train, y_train)" ], "metadata": { "id": "Vrz9CGfsEmA7", "colab": { "base_uri": "https://localhost:8080/", "height": 248 }, "outputId": "d34cdf86-7548-4a09-9f1c-dffd2c6357f9" }, "execution_count": 72, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "XGBRegressor(base_score=None, booster=None, callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, feature_types=None,\n", " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=None, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=None, max_leaves=None,\n", " min_child_weight=None, missing=nan, monotone_constraints=None,\n", " n_estimators=10, n_jobs=None, num_parallel_tree=None,\n", " predictor=None, random_state=None, ...)" ], "text/html": [ "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=10, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)
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" ] }, "metadata": {}, "execution_count": 72 } ] }, { "cell_type": "code", "source": [ "pred_xgb=xgb_r.predict(X_test)" ], "metadata": { "id": "oKvbjzC4Exf2" }, "execution_count": 73, "outputs": [] }, { "cell_type": "code", "source": [ "sns.distplot(y_test-pred_xgb)\n", "plt.show()" ], "metadata": { "id": "FCPBEUVZkzbW", "colab": { "base_uri": "https://localhost:8080/", "height": 645 }, "outputId": "4723bd9c-546d-4571-fcd1-e23c0d33ca1f" }, "execution_count": 74, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(y_test-pred_xgb)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.scatter(y_test, pred_xgb, alpha = 0.5)\n", "plt.xlabel(\"y_test\")\n", "plt.ylabel(\"y_pred\")\n", "plt.show()" ], "metadata": { "id": "L-y9SJzhk-gq", "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "outputId": "3b5bf0ac-97ac-4aca-d6bb-4ccee89fca76" }, "execution_count": 75, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n", "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n", "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))" ], "metadata": { "id": "A_ZVWOzwEoh1", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0d9ea5a0-a715-4ef2-f60d-749fde8a3e04" }, "execution_count": 76, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "MAE: 1187.2347040835286\n", "MSE: 3925052.529427591\n", "RMSE: 1981.1745328031022\n" ] } ] }, { "cell_type": "markdown", "source": [ "### HyperParameter Tuning" ], "metadata": { "id": "Ht9JT7jJE7wS" } }, { "cell_type": "code", "source": [ "max_depth=[5,10,15,20]\n", "n_estimators=[100,500,1000]\n", "n_jobs=[2,5,8,10]\n", "learning_rate=[0.005,0.01,0.05,0.1,1]" ], "metadata": { "id": "7mn6sF38E2E5" }, "execution_count": 77, "outputs": [] }, { "cell_type": "code", "source": [ "XGB_grid = {'max_depth': max_depth,\n", " 'n_estimators': n_estimators,\n", " 'max_depth': max_depth,\n", " 'learning_rate':learning_rate}" ], "metadata": { "id": "iMYYTVyaFrZM" }, "execution_count": 78, "outputs": [] }, { "cell_type": "code", "source": [ "XGB_random = RandomizedSearchCV(estimator = xgb_r, param_distributions = XGB_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42)" ], "metadata": { "id": "YMcxKxsOF5EM" }, "execution_count": 79, "outputs": [] }, { "cell_type": "code", "source": [ "XGB_random.fit(X_train,y_train)" ], "metadata": { "id": "ooX8BQ0DGG2v", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "5b6ea824-ce25-4a12-c3cb-bb7579284e37" }, "execution_count": 80, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n", "[CV] END .learning_rate=0.005, max_depth=5, n_estimators=100; total time= 0.9s\n", "[CV] END .learning_rate=0.005, max_depth=5, n_estimators=100; total time= 0.9s\n", "[CV] END .learning_rate=0.005, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END .learning_rate=0.005, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END .learning_rate=0.005, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END learning_rate=0.005, max_depth=10, n_estimators=1000; total time= 24.0s\n", "[CV] END learning_rate=0.005, max_depth=10, n_estimators=1000; total time= 26.4s\n", "[CV] END learning_rate=0.005, max_depth=10, n_estimators=1000; total time= 23.7s\n", "[CV] END learning_rate=0.005, max_depth=10, n_estimators=1000; total time= 21.5s\n", "[CV] END learning_rate=0.005, max_depth=10, n_estimators=1000; total time= 23.6s\n", "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 3.6s\n", "[CV] END ..learning_rate=0.1, max_depth=20, n_estimators=100; total time= 4.7s\n", "[CV] END ..learning_rate=0.1, max_depth=20, n_estimators=100; total time= 4.8s\n", "[CV] END ..learning_rate=0.1, max_depth=20, n_estimators=100; total time= 7.3s\n", "[CV] END ..learning_rate=0.1, max_depth=20, n_estimators=100; total time= 4.7s\n", "[CV] END ..learning_rate=0.1, max_depth=20, n_estimators=100; total time= 7.4s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time= 3.9s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time= 6.7s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time= 3.9s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time= 3.9s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time= 6.6s\n", "[CV] END ....learning_rate=1, max_depth=15, n_estimators=100; total time= 1.9s\n", "[CV] END ....learning_rate=1, max_depth=15, n_estimators=100; total time= 1.6s\n", "[CV] END ....learning_rate=1, max_depth=15, n_estimators=100; total time= 1.6s\n", "[CV] END ....learning_rate=1, max_depth=15, n_estimators=100; total time= 2.0s\n", "[CV] END ....learning_rate=1, max_depth=15, n_estimators=100; total time= 4.8s\n", "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=100; total time= 4.0s\n", "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=100; total time= 4.0s\n", "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=100; total time= 6.7s\n", "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=100; total time= 4.0s\n", "[CV] END .learning_rate=0.05, max_depth=20, n_estimators=100; total time= 4.0s\n", "[CV] END .....learning_rate=1, max_depth=5, n_estimators=100; total time= 3.6s\n", "[CV] END .....learning_rate=1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END .....learning_rate=1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END .....learning_rate=1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END .....learning_rate=1, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.8s\n", "[CV] END ....learning_rate=1, max_depth=20, n_estimators=100; total time= 1.6s\n", "[CV] END ....learning_rate=1, max_depth=20, n_estimators=100; total time= 4.3s\n", "[CV] END ....learning_rate=1, max_depth=20, n_estimators=100; total time= 1.9s\n", "[CV] END ....learning_rate=1, max_depth=20, n_estimators=100; total time= 1.7s\n", "[CV] END ....learning_rate=1, max_depth=20, n_estimators=100; total time= 2.0s\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "RandomizedSearchCV(cv=5,\n", " estimator=XGBRegressor(base_score=None, booster=None,\n", " callbacks=None,\n", " colsample_bylevel=None,\n", " colsample_bynode=None,\n", " colsample_bytree=None,\n", " early_stopping_rounds=None,\n", " enable_categorical=False,\n", " eval_metric=None, feature_types=None,\n", " gamma=None, gpu_id=None,\n", " grow_policy=None,\n", " importance_type=None,\n", " interaction_constraints=None,\n", " learning_rate=...\n", " max_delta_step=None, max_depth=None,\n", " max_leaves=None,\n", " min_child_weight=None, missing=nan,\n", " monotone_constraints=None,\n", " n_estimators=10, n_jobs=None,\n", " num_parallel_tree=None,\n", " predictor=None, random_state=None, ...),\n", " param_distributions={'learning_rate': [0.005, 0.01, 0.05,\n", " 0.1, 1],\n", " 'max_depth': [5, 10, 15, 20],\n", " 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" ] }, "metadata": {}, "execution_count": 80 } ] }, { "cell_type": "code", "source": [ "XGB_random.best_params_" ], "metadata": { "id": "yJpKjfXQGeB1", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "15fb3883-9b9c-48be-961e-659fca50631a" }, "execution_count": 81, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'n_estimators': 1000, 'max_depth': 10, 'learning_rate': 0.005}" ] }, "metadata": {}, "execution_count": 81 } ] }, { "cell_type": "code", "source": [ "xgb_best=xg.XGBRegressor(learning_rate= 0.005, max_depth= 10, n_estimators= 1000,state=42,objectvie='reg:squarederror')" ], "metadata": { "id": "-sDneAxnHSxR" }, "execution_count": 82, "outputs": [] }, { "cell_type": "code", "source": [ "xgb_best.fit(X_train,y_train)" ], "metadata": { "id": "WNszDfEgH4Ej", "colab": { "base_uri": "https://localhost:8080/", "height": 302 }, "outputId": "3d1863ea-b66e-4956-d22c-df5943fdeb31" }, "execution_count": 83, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[07:04:11] WARNING: ../src/learner.cc:767: \n", "Parameters: { \"objectvie\", \"state\" } are not used.\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "XGBRegressor(base_score=None, booster=None, callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, feature_types=None,\n", " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=0.005, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=10, max_leaves=None,\n", " min_child_weight=None, missing=nan, monotone_constraints=None,\n", " n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n", " objectvie='reg:squarederror', predictor=None, ...)" ], "text/html": [ "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
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              "             objectvie='reg:squarederror', predictor=None, ...)
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" ] }, "metadata": {}, "execution_count": 83 } ] }, { "cell_type": "code", "source": [ "y_prediction_xgb = xgb_best.predict(X_test)" ], "metadata": { "id": "cxM78lGNHblm" }, "execution_count": 84, "outputs": [] }, { "cell_type": "code", "source": [ "metrics.r2_score(y_test, y_prediction_xgb)" ], "metadata": { "id": "J8tABRW-H0Fq", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "168c4b22-11a9-4d95-a2ef-ecf8dc571bc3" }, "execution_count": 85, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8401304475145384" ] }, "metadata": {}, "execution_count": 85 } ] }, { "cell_type": "markdown", "source": [ "## Linear Regression" ], "metadata": { "id": "MvI2_OMkWaHg" } }, { "cell_type": "code", "source": [ "from sklearn.linear_model import LinearRegression" ], "metadata": { "id": "xdpGBg1KWIKd" }, "execution_count": 86, "outputs": [] }, { "cell_type": "code", "source": [ "lr=LinearRegression()\n", "lr.fit(X_train,y_train)" ], "metadata": { "id": "xASxHV4lXf83", "colab": { "base_uri": "https://localhost:8080/", "height": 75 }, "outputId": "f7df8f6b-334e-441a-97d2-73d7615228be" }, "execution_count": 87, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearRegression()" ], "text/html": [ "
LinearRegression()
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" ] }, "metadata": {}, "execution_count": 87 } ] }, { "cell_type": "code", "source": [ "lr_pred=lr.predict(X_test)" ], "metadata": { "id": "Mux4xF-vXnYF" }, "execution_count": 88, "outputs": [] }, { "cell_type": "code", "source": [ "sns.distplot(lr_pred)\n", "plt.show()" ], "metadata": { "id": "P5N7DZaeiejW", "colab": { "base_uri": "https://localhost:8080/", "height": 626 }, "outputId": "86b09be7-e921-44f1-bfb0-f15e6d268356" }, "execution_count": 89, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: UserWarning: \n", "\n", "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", "\n", "Please adapt your code to use either `displot` (a figure-level function with\n", "similar flexibility) or `histplot` (an axes-level function for histograms).\n", "\n", "For a guide to updating your code to use the new functions, please see\n", "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", "\n", " sns.distplot(lr_pred)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "plt.scatter(y_test,lr_pred, alpha = 0.5)\n", "plt.xlabel(\"y_test\")\n", "plt.ylabel(\"y_pred\")\n", "plt.show()" ], "metadata": { "id": "53lHUweOidoi", "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "outputId": "836634d7-d151-4018-9714-c842b882137e" }, "execution_count": 90, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "metrics.r2_score(y_test, lr_pred)" ], "metadata": { "id": "yL_Zc2h5XxtJ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "846e10c8-19f2-4e9f-d59f-3d0ab7fa5204" }, "execution_count": 91, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.5837544362210152" ] }, "metadata": {}, "execution_count": 91 } ] }, { "cell_type": "code", "source": [ "import joblib\n", "joblib.dump(xgb_r, 'flight_price_model.pkl')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PjHGoNM9PSM5", "outputId": "7906485d-0ddb-4f00-ae3d-23c2180fbad4" }, "execution_count": 93, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['flight_price_model.pkl']" ] }, "metadata": {}, "execution_count": 93 } ] } ] }