{ "cells": [ { "cell_type": "markdown", "id": "cec5d065", "metadata": {}, "source": [ "# ONLINE APP RESTAURANT RATING PREDICTION" ] }, { "cell_type": "markdown", "id": "8328fa2a", "metadata": {}, "source": [ "The data from an online food app, which needs assistance in predicting the future success or failure of a business (restaurant), has been used in this case study. Such that they can choose whether to delete the restaurant from their app or keep it. They have provided information from 9551 eateries from across the world that are currently accessible on their app. It contains details about the restaurants, including the overall rating.\n", "\n", "I am planning to develop a machine learning model that can forecast a restaurant's rating based on its attributes.\n", "\n", "In the case study that follows, I'll go over the step-by-step process for developing a machine learning prediction model in such circumstances." ] }, { "cell_type": "markdown", "id": "d628982b", "metadata": {}, "source": [ "\n", "### The flow of the case study is as below:\n", "\n", "\n", "The flow of the case study is as below:\n", "* Reading the data in python\n", "* Defining the problem statement\n", "* Identifying the Target variable\n", "* Looking at the distribution of Target variable\n", "* Basic Data exploration\n", "* Rejecting useless columns\n", "* Visual Exploratory Data Analysis for data distribution (Histogram and Barcharts)\n", "* Outlier treatment\n", "* Missing Values treatment\n", "* Visual correlation analysis\n", "* Statistical correlation analysis (Feature Selection)\n", "* Converting data to numeric for ML\n", "* Sampling and K-fold cross validation\n", "* Trying multiple Regression algorithms\n", "* Selecting the best Model\n", "* Deploying the best model using streamlit" ] }, { "cell_type": "markdown", "id": "c1562ae0", "metadata": {}, "source": [ "## Data description\n", " The business meaning of each column in the data is as below\n", "\n", "* Restaurant ID: The id for each restaurant\n", "*Restaurant Name: The brand/restaurant name\n", "*Country Code: In which country the restaurant is operating\n", "*City: In which city the restaurant is operating\n", "*Address: What is the address of the restaurant\n", "*Locality: What is the locality of the restaurant\n", "*Locality Verbose: Detailed locality description\n", "*Longitude: GPS longitude location\n", "*Latitude: GPS latitude location\n", "*Cuisines: Various type of food offered\n", "*Currency: The business currency\n", "*Has Table booking: Is advance table booking facility available?\n", "*Has Online delivery: Does they take online food orders?\n", "*Is delivering now: Is is open now?\n", "*Switch to order menu: Whether switch to order menu is available?\n", "*Price range: The price range of the restaurant\n", "*Votes: The number of people who voted for the rating\n", "*Average Cost for two: The typical cost for two people\n", "*Rating: The final rating of the restaurant" ] }, { "cell_type": "code", "execution_count": 1, "id": "4bd0c1a9", "metadata": {}, "outputs": [], "source": [ "# Supressing the warning messages\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "id": "8da02445", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape before deleting duplicate values: (9551, 19)\n", "Shape After deleting duplicate values: (9551, 19)\n" ] }, { "data": { "text/html": [ "
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Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeVotesAverage Cost for twoRating
06317637Le Petit Souffle162Makati CityThird Floor, Century City Mall, Kalayaan Avenu...Century City Mall, Poblacion, Makati CityCentury City Mall, Poblacion, Makati City, Mak...121.02753514.565443French, Japanese, DessertsBotswana Pula(P)YesNoNoNo331411004.8
16304287Izakaya Kikufuji162Makati CityLittle Tokyo, 2277 Chino Roces Avenue, Legaspi...Little Tokyo, Legaspi Village, Makati CityLittle Tokyo, Legaspi Village, Makati City, Ma...121.01410114.553708JapaneseBotswana Pula(P)YesNoNoNo359112004.5
26300002Heat - Edsa Shangri-La162Mandaluyong CityEdsa Shangri-La, 1 Garden Way, Ortigas, Mandal...Edsa Shangri-La, Ortigas, Mandaluyong CityEdsa Shangri-La, Ortigas, Mandaluyong City, Ma...121.05683114.581404Seafood, Asian, Filipino, IndianBotswana Pula(P)YesNoNoNo427040004.4
36318506Ooma162Mandaluyong CityThird Floor, Mega Fashion Hall, SM Megamall, O...SM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong City, Mandal...121.05647514.585318Japanese, SushiBotswana Pula(P)NoNoNoNo436515004.9
46314302Sambo Kojin162Mandaluyong CityThird Floor, Mega Atrium, SM Megamall, Ortigas...SM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong City, Mandal...121.05750814.584450Japanese, KoreanBotswana Pula(P)YesNoNoNo422915004.8
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" ], "text/plain": [ " Restaurant ID Restaurant Name Country Code City \\\n", "0 6317637 Le Petit Souffle 162 Makati City \n", "1 6304287 Izakaya Kikufuji 162 Makati City \n", "2 6300002 Heat - Edsa Shangri-La 162 Mandaluyong City \n", "3 6318506 Ooma 162 Mandaluyong City \n", "4 6314302 Sambo Kojin 162 Mandaluyong City \n", "\n", " Address \\\n", "0 Third Floor, Century City Mall, Kalayaan Avenu... \n", "1 Little Tokyo, 2277 Chino Roces Avenue, Legaspi... \n", "2 Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal... \n", "3 Third Floor, Mega Fashion Hall, SM Megamall, O... \n", "4 Third Floor, Mega Atrium, SM Megamall, Ortigas... \n", "\n", " Locality \\\n", "0 Century City Mall, Poblacion, Makati City \n", "1 Little Tokyo, Legaspi Village, Makati City \n", "2 Edsa Shangri-La, Ortigas, Mandaluyong City \n", "3 SM Megamall, Ortigas, Mandaluyong City \n", "4 SM Megamall, Ortigas, Mandaluyong City \n", "\n", " Locality Verbose Longitude Latitude \\\n", "0 Century City Mall, Poblacion, Makati City, Mak... 121.027535 14.565443 \n", "1 Little Tokyo, Legaspi Village, Makati City, Ma... 121.014101 14.553708 \n", "2 Edsa Shangri-La, Ortigas, Mandaluyong City, Ma... 121.056831 14.581404 \n", "3 SM Megamall, Ortigas, Mandaluyong City, Mandal... 121.056475 14.585318 \n", "4 SM Megamall, Ortigas, Mandaluyong City, Mandal... 121.057508 14.584450 \n", "\n", " Cuisines Currency Has Table booking \\\n", "0 French, Japanese, Desserts Botswana Pula(P) Yes \n", "1 Japanese Botswana Pula(P) Yes \n", "2 Seafood, Asian, Filipino, Indian Botswana Pula(P) Yes \n", "3 Japanese, Sushi Botswana Pula(P) No \n", "4 Japanese, Korean Botswana Pula(P) Yes \n", "\n", " Has Online delivery Is delivering now Switch to order menu Price range \\\n", "0 No No No 3 \n", "1 No No No 3 \n", "2 No No No 4 \n", "3 No No No 4 \n", "4 No No No 4 \n", "\n", " Votes Average Cost for two Rating \n", "0 314 1100 4.8 \n", "1 591 1200 4.5 \n", "2 270 4000 4.4 \n", "3 365 1500 4.9 \n", "4 229 1500 4.8 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Reading the dataset\n", "import pandas as pd\n", "import numpy as np \n", "RtData = pd.read_csv('Restaurant_Data.csv', encoding='latin')\n", "print('Shape before deleting duplicate values:', RtData.shape)\n", "\n", "# Removing duplicate rows if any\n", "RtData=RtData.drop_duplicates()\n", "print('Shape After deleting duplicate values:', RtData.shape)\n", "\n", "# Printing sample data\n", "# Start observing the Quantitative/Categorical/Qualitative variables\n", "RtData.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "id": "8d9f8c51", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Restaurant IDCountry CodeLongitudeLatitudePrice rangeVotesAverage Cost for twoRating
count9.551000e+039551.0000009551.0000009551.0000009551.0000009551.0000009551.0000009551.000000
mean9.051128e+0618.36561664.12657425.8543811.804837156.9097481199.2107632.891268
std8.791521e+0656.75054641.46705811.0079350.905609430.16914516121.1830731.128845
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50%6.004089e+061.00000077.19196428.5704692.00000031.000000400.0000003.200000
75%1.835229e+071.00000077.28200628.6427582.000000131.000000700.0000003.700000
max1.850065e+07216.000000174.83208955.9769804.00000010934.000000800000.0000004.900000
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" ], "text/plain": [ " Restaurant ID Country Code Longitude Latitude Price range \\\n", "count 9.551000e+03 9551.000000 9551.000000 9551.000000 9551.000000 \n", "mean 9.051128e+06 18.365616 64.126574 25.854381 1.804837 \n", "std 8.791521e+06 56.750546 41.467058 11.007935 0.905609 \n", "min 5.300000e+01 1.000000 -157.948486 -41.330428 1.000000 \n", "25% 3.019625e+05 1.000000 77.081343 28.478713 1.000000 \n", "50% 6.004089e+06 1.000000 77.191964 28.570469 2.000000 \n", "75% 1.835229e+07 1.000000 77.282006 28.642758 2.000000 \n", "max 1.850065e+07 216.000000 174.832089 55.976980 4.000000 \n", "\n", " Votes Average Cost for two Rating \n", "count 9551.000000 9551.000000 9551.000000 \n", "mean 156.909748 1199.210763 2.891268 \n", "std 430.169145 16121.183073 1.128845 \n", "min 0.000000 0.000000 1.000000 \n", "25% 5.000000 250.000000 2.500000 \n", "50% 31.000000 400.000000 3.200000 \n", "75% 131.000000 700.000000 3.700000 \n", "max 10934.000000 800000.000000 4.900000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RtData.describe()" ] }, { "cell_type": "code", "execution_count": 4, "id": "37e42d63", "metadata": {}, "outputs": [], "source": [ "#Selecting the restaurants located in India \n", "RtData = RtData[(RtData.Currency == \"Indian Rupees(Rs.)\")]" ] }, { "cell_type": "code", "execution_count": 5, "id": "b26faa84", "metadata": {}, "outputs": [], "source": [ "#Removing the data where Average cost is 0\n", "RtData = RtData.loc[(RtData['Average Cost for two'] > 0)]" ] }, { "cell_type": "code", "execution_count": 6, "id": "99a60b78", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(8643, 19)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RtData.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "96eaaad0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Restaurant IDCountry CodeLongitudeLatitudePrice rangeVotesAverage Cost for twoRating
count8.643000e+038643.08643.0000008643.0000008643.0000008643.0000008643.0000008643.000000
mean8.655738e+061.072.86789626.4263651.722434137.270045624.0194382.769374
std8.960763e+060.017.9915966.9684680.852669428.257651595.6462581.105049
min5.300000e+011.00.0000000.0000001.0000000.00000050.0000001.000000
25%3.007115e+051.077.09879128.4922601.0000004.000000300.0000002.100000
50%2.100861e+061.077.20335128.5699062.00000024.000000450.0000003.100000
75%1.836121e+071.077.28506528.6379502.000000100.000000700.0000003.600000
max1.850065e+071.091.80649335.0000004.00000010934.0000008000.0000004.900000
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" ], "text/plain": [ " Restaurant ID Country Code Longitude Latitude Price range \\\n", "count 8.643000e+03 8643.0 8643.000000 8643.000000 8643.000000 \n", "mean 8.655738e+06 1.0 72.867896 26.426365 1.722434 \n", "std 8.960763e+06 0.0 17.991596 6.968468 0.852669 \n", "min 5.300000e+01 1.0 0.000000 0.000000 1.000000 \n", "25% 3.007115e+05 1.0 77.098791 28.492260 1.000000 \n", "50% 2.100861e+06 1.0 77.203351 28.569906 2.000000 \n", "75% 1.836121e+07 1.0 77.285065 28.637950 2.000000 \n", "max 1.850065e+07 1.0 91.806493 35.000000 4.000000 \n", "\n", " Votes Average Cost for two Rating \n", "count 8643.000000 8643.000000 8643.000000 \n", "mean 137.270045 624.019438 2.769374 \n", "std 428.257651 595.646258 1.105049 \n", "min 0.000000 50.000000 1.000000 \n", "25% 4.000000 300.000000 2.100000 \n", "50% 24.000000 450.000000 3.100000 \n", "75% 100.000000 700.000000 3.600000 \n", "max 10934.000000 8000.000000 4.900000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RtData.describe()" ] }, { "cell_type": "markdown", "id": "660a209f", "metadata": {}, "source": [ "## Defining the problem statement:\n", "#### Create a Predictive model which can predict the future Rating of a restaurant\n", "\n", "* Target Variable: Rating\n", "* Predictors: location, menu, cost etc.\n", " \n", "* Rating=1 Worst\n", "* Rating=5 Best" ] }, { "cell_type": "markdown", "id": "38824774", "metadata": {}, "source": [ "## Determining the type of Machine Learning\n", "Based on the problem statement you can understand that we need to create a supervised ML Regression model, as the target variable is Continuous." ] }, { "cell_type": "markdown", "id": "235825f2", "metadata": {}, "source": [ "## Looking at the distribution of Target variable" ] }, { "cell_type": "code", "execution_count": 8, "id": "b8a3c8bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "#Creating Bar chart as the Target Variable is Continuous\n", "RtData['Rating'].hist()" ] }, { "cell_type": "markdown", "id": "33120446", "metadata": {}, "source": [ "\n", "The data distribution of the target variable is satisfactory to proceed further. There are sufficient number of rows for each type of values to learn from." ] }, { "cell_type": "markdown", "id": "b129d10b", "metadata": {}, "source": [ "## Basic Data Exploration" ] }, { "cell_type": "code", "execution_count": 9, "id": "263f4523", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 8643 entries, 624 to 9275\n", "Data columns (total 19 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Restaurant ID 8643 non-null int64 \n", " 1 Restaurant Name 8643 non-null object \n", " 2 Country Code 8643 non-null int64 \n", " 3 City 8643 non-null object \n", " 4 Address 8643 non-null object \n", " 5 Locality 8643 non-null object \n", " 6 Locality Verbose 8643 non-null object \n", " 7 Longitude 8643 non-null float64\n", " 8 Latitude 8643 non-null float64\n", " 9 Cuisines 8643 non-null object \n", " 10 Currency 8643 non-null object \n", " 11 Has Table booking 8643 non-null object \n", " 12 Has Online delivery 8643 non-null object \n", " 13 Is delivering now 8643 non-null object \n", " 14 Switch to order menu 8643 non-null object \n", " 15 Price range 8643 non-null int64 \n", " 16 Votes 8643 non-null int64 \n", " 17 Average Cost for two 8643 non-null int64 \n", " 18 Rating 8643 non-null float64\n", "dtypes: float64(3), int64(5), object(11)\n", "memory usage: 1.3+ MB\n" ] } ], "source": [ "#Looking at the summarized information of the data\n", "RtData.info()" ] }, { "cell_type": "code", "execution_count": 10, "id": "fd38a5c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeVotesAverage Cost for twoRating
count8.643000e+0386438643.086438643864386438643.0000008643.0000008643864386438643864386438643.0000008643.0000008643.0000008643.000000
uniqueNaN6595NaN438017783840NaNNaN138912221NaNNaNNaNNaN
topNaNCafe Coffee DayNaNNew DelhiDilli Haat, INA, New DelhiConnaught PlaceConnaught Place, New DelhiNaNNaNNorth IndianIndian Rupees(Rs.)NoNoNoNoNaNNaNNaNNaN
freqNaN83NaN547311122122NaNNaN93686437532622086098643NaNNaNNaNNaN
mean8.655738e+06NaN1.0NaNNaNNaNNaN72.86789626.426365NaNNaNNaNNaNNaNNaN1.722434137.270045624.0194382.769374
std8.960763e+06NaN0.0NaNNaNNaNNaN17.9915966.968468NaNNaNNaNNaNNaNNaN0.852669428.257651595.6462581.105049
min5.300000e+01NaN1.0NaNNaNNaNNaN0.0000000.000000NaNNaNNaNNaNNaNNaN1.0000000.00000050.0000001.000000
25%3.007115e+05NaN1.0NaNNaNNaNNaN77.09879128.492260NaNNaNNaNNaNNaNNaN1.0000004.000000300.0000002.100000
50%2.100861e+06NaN1.0NaNNaNNaNNaN77.20335128.569906NaNNaNNaNNaNNaNNaN2.00000024.000000450.0000003.100000
75%1.836121e+07NaN1.0NaNNaNNaNNaN77.28506528.637950NaNNaNNaNNaNNaNNaN2.000000100.000000700.0000003.600000
max1.850065e+07NaN1.0NaNNaNNaNNaN91.80649335.000000NaNNaNNaNNaNNaNNaN4.00000010934.0000008000.0000004.900000
\n", "
" ], "text/plain": [ " Restaurant ID Restaurant Name Country Code City \\\n", "count 8.643000e+03 8643 8643.0 8643 \n", "unique NaN 6595 NaN 43 \n", "top NaN Cafe Coffee Day NaN New Delhi \n", "freq NaN 83 NaN 5473 \n", "mean 8.655738e+06 NaN 1.0 NaN \n", "std 8.960763e+06 NaN 0.0 NaN \n", "min 5.300000e+01 NaN 1.0 NaN \n", "25% 3.007115e+05 NaN 1.0 NaN \n", "50% 2.100861e+06 NaN 1.0 NaN \n", "75% 1.836121e+07 NaN 1.0 NaN \n", "max 1.850065e+07 NaN 1.0 NaN \n", "\n", " Address Locality \\\n", "count 8643 8643 \n", "unique 8017 783 \n", "top Dilli Haat, INA, New Delhi Connaught Place \n", "freq 11 122 \n", "mean NaN NaN \n", "std NaN NaN \n", "min NaN NaN \n", "25% NaN NaN \n", "50% NaN NaN \n", "75% NaN NaN \n", "max NaN NaN \n", "\n", " Locality Verbose Longitude Latitude Cuisines \\\n", "count 8643 8643.000000 8643.000000 8643 \n", "unique 840 NaN NaN 1389 \n", "top Connaught Place, New Delhi NaN NaN North Indian \n", "freq 122 NaN NaN 936 \n", "mean NaN 72.867896 26.426365 NaN \n", "std NaN 17.991596 6.968468 NaN \n", "min NaN 0.000000 0.000000 NaN \n", "25% NaN 77.098791 28.492260 NaN \n", "50% NaN 77.203351 28.569906 NaN \n", "75% NaN 77.285065 28.637950 NaN \n", "max NaN 91.806493 35.000000 NaN \n", "\n", " Currency Has Table booking Has Online delivery \\\n", "count 8643 8643 8643 \n", "unique 1 2 2 \n", "top Indian Rupees(Rs.) No No \n", "freq 8643 7532 6220 \n", "mean NaN NaN NaN \n", "std NaN NaN NaN \n", "min NaN NaN NaN \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max NaN NaN NaN \n", "\n", " Is delivering now Switch to order menu Price range Votes \\\n", "count 8643 8643 8643.000000 8643.000000 \n", "unique 2 1 NaN NaN \n", "top No No NaN NaN \n", "freq 8609 8643 NaN NaN \n", "mean NaN NaN 1.722434 137.270045 \n", "std NaN NaN 0.852669 428.257651 \n", "min NaN NaN 1.000000 0.000000 \n", "25% NaN NaN 1.000000 4.000000 \n", "50% NaN NaN 2.000000 24.000000 \n", "75% NaN NaN 2.000000 100.000000 \n", "max NaN NaN 4.000000 10934.000000 \n", "\n", " Average Cost for two Rating \n", "count 8643.000000 8643.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 624.019438 2.769374 \n", "std 595.646258 1.105049 \n", "min 50.000000 1.000000 \n", "25% 300.000000 2.100000 \n", "50% 450.000000 3.100000 \n", "75% 700.000000 3.600000 \n", "max 8000.000000 4.900000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Looking at the descriptive statistics of the data\n", "RtData.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 11, "id": "7dcadd76", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Restaurant ID 8643\n", "Restaurant Name 6595\n", "Country Code 1\n", "City 43\n", "Address 8017\n", "Locality 783\n", "Locality Verbose 840\n", "Longitude 7224\n", "Latitude 7784\n", "Cuisines 1389\n", "Currency 1\n", "Has Table booking 2\n", "Has Online delivery 2\n", "Is delivering now 2\n", "Switch to order menu 1\n", "Price range 4\n", "Votes 871\n", "Average Cost for two 78\n", "Rating 33\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Finging unique values for each column\n", "# T0 understand which column is categorical and which one is Continuous\n", "# Typically if the numer of unique values are < 20 then the variable is likely to be a category otherwise continuous\n", "RtData.nunique()" ] }, { "cell_type": "markdown", "id": "672b6703", "metadata": {}, "source": [ "## Basic Data Exploration Results\n", "Based on the basic exploration above, you can now create a simple report of the data, noting down your observations regaring each column. Hence, creating a initial roadmap for further analysis.\n", "\n", "The selected columns in this step are not final, further study will be done and then a final list will be created\n", "\n", "* Restaurant ID: Qualitative. Rejected.\n", "* Restaurant Name: Qualitative. Rejected.\n", "* Country Code: Categorical. Selected.\n", "* City: Categorical. Rejected. Too many unique levels, hence it will cause high dimensionality.\n", "* Address: Qualitative. Rejected.\n", "* Locality: Qualitative. Rejected.\n", "* Locality Verbose: Qualitative. Rejected.\n", "* Longitude: Continuous. Selected. This represents the location of restaurant\n", "* Latitude: Continuous. Selected. This represents the location of restaurant\n", "* Cuisines: Qualitative. Rejected.Too many unique levels, hence it will cause high dimensionality.\n", "* Currency: Categorical. Selected.\n", "* Has Table booking: Categorical. Selected.\n", "* Has Online delivery: Categorical. Selected.\n", "* Is delivering now: Categorical. Selected.\n", "* Switch to order menu: Categorical. Selected.\n", "* Price range: Categorical. Selected.\n", "* Votes: Continuous. Selected.\n", "* Average Cost for two: Continuous. Selected.\n", "* Rating: Continuous. Selected. This is the Target Variable!" ] }, { "cell_type": "markdown", "id": "0baf7182", "metadata": {}, "source": [ "## Removing useless columns from the data" ] }, { "cell_type": "markdown", "id": "b8973615", "metadata": {}, "source": [ "\n", "Dates, addresses, and other qualitative columns such as these cannot be used directly for machine learning. As a result, each row has a unique string value, making it impossible for machine learning algorithms to learn anything from them. Simply put, such columns cannot be used to derive any general rules.\n", "\n", "However, we are able to extract some data from these columns that can be applied to ML. For example, we can extract the month, week, quarter, etc. from a date column to create a category feature but it will not be that useful for this project.\n", "\n", "Similarly, we can extract certain repetitious data from addresses, such as zip codes, location, etc., but if the number of unique values is too high (more than 50), it causes problems for the ML algorithm later on as the data dimensionality rises while converting such columns to dummy variables.\n", "\n", "The \"Cuisines\" column in this set of data cannot be used directly because it will produce 1825 dummy variables! so, removing that as well.\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "a391dd7c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LongitudeLatitudeHas Table bookingHas Online deliveryIs delivering nowPrice rangeVotesAverage Cost for twoRating
62478.01154427.161661NoNoNo31408503.9
6250.0000000.000000NoNoNo2717003.5
62678.01160827.160832NoNoNo2945003.6
62777.99809227.195928NoNoNo2874004.0
62878.00755327.201725NoNoNo317710004.2
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" ], "text/plain": [ " Longitude Latitude Has Table booking Has Online delivery \\\n", "624 78.011544 27.161661 No No \n", "625 0.000000 0.000000 No No \n", "626 78.011608 27.160832 No No \n", "627 77.998092 27.195928 No No \n", "628 78.007553 27.201725 No No \n", "\n", " Is delivering now Price range Votes Average Cost for two Rating \n", "624 No 3 140 850 3.9 \n", "625 No 2 71 700 3.5 \n", "626 No 2 94 500 3.6 \n", "627 No 2 87 400 4.0 \n", "628 No 3 177 1000 4.2 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Deleting those columns which are not useful in predictive analysis because these variables are qualitative\n", "UselessColumns = ['Restaurant ID', 'Restaurant Name','City','Address',\n", " 'Locality', 'Locality Verbose','Cuisines','Country Code', 'Currency','Switch to order menu']\n", "RtData = RtData.drop(UselessColumns,axis=1)\n", "RtData.head()" ] }, { "cell_type": "code", "execution_count": 13, "id": "adf061f2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LongitudeLatitudePrice rangeVotesAverage Cost for twoRating
count8643.0000008643.0000008643.0000008643.0000008643.0000008643.000000
mean72.86789626.4263651.722434137.270045624.0194382.769374
std17.9915966.9684680.852669428.257651595.6462581.105049
min0.0000000.0000001.0000000.00000050.0000001.000000
25%77.09879128.4922601.0000004.000000300.0000002.100000
50%77.20335128.5699062.00000024.000000450.0000003.100000
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" ], "text/plain": [ " Longitude Latitude Price range Votes \\\n", "count 8643.000000 8643.000000 8643.000000 8643.000000 \n", "mean 72.867896 26.426365 1.722434 137.270045 \n", "std 17.991596 6.968468 0.852669 428.257651 \n", "min 0.000000 0.000000 1.000000 0.000000 \n", "25% 77.098791 28.492260 1.000000 4.000000 \n", "50% 77.203351 28.569906 2.000000 24.000000 \n", "75% 77.285065 28.637950 2.000000 100.000000 \n", "max 91.806493 35.000000 4.000000 10934.000000 \n", "\n", " Average Cost for two Rating \n", "count 8643.000000 8643.000000 \n", "mean 624.019438 2.769374 \n", "std 595.646258 1.105049 \n", "min 50.000000 1.000000 \n", "25% 300.000000 2.100000 \n", "50% 450.000000 3.100000 \n", "75% 700.000000 3.600000 \n", "max 8000.000000 4.900000 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RtData.describe()" ] }, { "cell_type": "markdown", "id": "7fecea41", "metadata": {}, "source": [ "# Visual Exploratory Data Analysis\n", "* Categorical variables: Bar plot\n", "* Continuous variables: Histogram" ] }, { "cell_type": "markdown", "id": "767d6898", "metadata": {}, "source": [ "## Visualizing the distribution of all the Categorical Predictor variables in the data using bar plots\n", "\n", "Categorical Predictors: 'Country Code', 'Currency', 'Has Table booking', 'Has Online delivery', 'Is delivering now', 'Switch to order menu','Price range'\n", "\n", "We use bar charts to see how the data is distributed for these categorical columns." ] }, { "cell_type": "code", "execution_count": 14, "id": "c5794252", "metadata": {}, "outputs": [], "source": [ "# Plotting multiple bar charts at once for categorical variables\n", "# Since there is no default function which can plot bar charts for multiple columns at once\n", "# defining my own function for the same\n", "\n", "def PlotBarCharts(inpData, colsToPlot):\n", " %matplotlib inline\n", " \n", " import matplotlib.pyplot as plt \n", " \n", " #Generating multiple subplots\n", " fig, subPlot=plt.subplots(nrows=1, ncols=len(colsToPlot), figsize=(20,5))\n", " fig.suptitle('Bar charts of:' + str(colsToPlot))\n", " \n", " for colName, plotNumber in zip(colsToPlot, range(len(colsToPlot))):\n", " inpData.groupby(colName).size().plot(kind='bar', ax=subPlot[plotNumber])" ] }, { "cell_type": "code", "execution_count": 15, "id": "5835bbad", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#####################################################################\n", "# Calling the function\n", "PlotBarCharts(inpData=RtData, colsToPlot=['Has Table booking', 'Has Online delivery', 'Is delivering now','Price range'])" ] }, { "cell_type": "markdown", "id": "063f43d0", "metadata": {}, "source": [ "In this data, \"Country Code\", \"Currency\", \"is delivering now\" and \"Switch to order menu\" are too skewed. There is just one bar which is dominating and other categories have very less rows or there is just one value only. Such columns are not correlated with the target variable because there is no information to learn. The algorithms cannot find any rule like when the value is this then the target variable is that.\n", "\n", "Selected Categorical Variables: Only three categorical variables are selected for further analysis.\n", "\n", "'Has Table booking', 'Has Online delivery', 'Price range'" ] }, { "cell_type": "markdown", "id": "5ebe0e11", "metadata": {}, "source": [ "## Visualizing distribution of all the Continuous Predictor variables in the data using histograms\n", "Based on the Basic Data Exploration, There are four continuous predictor variables ''Longitude', 'Latitude','Votes', and 'Average Cost for two'" ] }, { "cell_type": "code", "execution_count": 16, "id": "75e6a92d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ],\n", " [,\n", " ]],\n", " dtype=object)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SpIFjsCpJ0jKWZHeSnUnuTnJnSzs+yc1JHmjvx3Wtf3mSXUnuT3JuV/qZbT+7klyVJEtRHknS6DBYlSRJP1lVZ1TV2vZ5M3BLVa0BbmmfSXIKsAE4FVgHvDPJEW2bq4FNwJr2WtfH/EuSRpDBqiRJmmo9sK0tbwPO60q/vqoer6oHgV3AWUlWAsdU1a1VVcC1XdtIkjQvBquSJC1vBXw8yV1JNrW0saraB9DeT2jpq4CHu7bd09JWteWp6ZIkzduRS50BSZK0pF5RVXuTnADcnORzh1h3unGodYj0p27cCYY3AYyNjTExMTGP7D7VgQMHerKfpTYq5YDRKcuolANGpyxLVY7LTj/Y0/2NrVjYPgfpt1zs32RWwWqS3cBjwBPAwapam+R44P3AScBu4PVV9fW2/uXAxW39N1fVx1r6mcA1wArgI8ClrbuQJElaAlW1t73vT/Ih4CzgkSQrq2pf6+K7v62+Bzixa/PVwN6Wvnqa9KnH2gpsBVi7dm2Nj48vOP8TExP0Yj9LbVTKAaNTllEpB4xOWZaqHBdtvrGn+7vs9INcuXP+9wx3XzDeu8ws0GL/JnPpBuzkC5IkjZAkRyd57uQy8NPAZ4EdwMa22kbghra8A9iQ5KgkJ9Npy+9oXYUfS3J2mwX4wq5tJEmal4V0A14PjLflbcAE8Ba6Jl8AHkwyOfnCbtrkCwBJJidfuGkBeZAkSfM3BnyoPWXmSOC9VfXRJJ8Etie5GHgIOB+gqu5Jsh24FzgIXFJVT7R9vYkne0/dhO27JGmBZhusTk6+UMAft248T5l8oY11gc6ECrd1bTs5ycJ3cPIFSZIGRlV9AXjZNOlfA86ZYZstwJZp0u8ETut1HiVJy9dsg9W+Tb4AvZ+AYVQGlfeTdTZ31tncjVqd9XoChunMdlKGUapXSZK0PM0qWO3n5AvtOD2dgGFUBpX3k3U2d9bZ3I1anfV6AobpzHZShkGafEGSJGk+DjvBkpMvSJIkSZL6bTZ3Vp18QZIkSZLUV4cNVp18QZIkSZLUb3N5zqokSZIkSX1hsCpJkiRJGjgGq5IkSZKkgWOwKkmSJEkaOAarkiRJkqSBY7AqSZIkSRo4BquSJEmSpIFjsCpJkiRJGjgGq5IkSZKkgWOwKkmSJEkaOAarkiQtY0mOSPLpJB9un49PcnOSB9r7cV3rXp5kV5L7k5zblX5mkp3tu6uSZCnKIkkaLQarkiQtb5cC93V93gzcUlVrgFvaZ5KcAmwATgXWAe9MckTb5mpgE7Cmvdb1J+uSpFFmsCpJ0jKVZDXwGuBdXcnrgW1teRtwXlf69VX1eFU9COwCzkqyEjimqm6tqgKu7dpGkqR5O3KpMyBJkpbM24BfB57blTZWVfsAqmpfkhNa+irgtq719rS077TlqelPk2QTnTuwjI2NMTExseACHDhwoCf7WWqjUg4YnbKMSjlgdMqyVOW47PSDPd3f2IqF7XOQfsvF/k1mHay2rj53Al+qqtcmOR54P3ASsBt4fVV9va17OXAx8ATw5qr6WEs/E7gGWAF8BLi0XYWVJEl9lOS1wP6quivJ+Gw2mSatDpH+9MSqrcBWgLVr19b4+GwOe2gTExP0Yj9LbVTKAaNTllEpB4xOWZaqHBdtvrGn+7vs9INcuXP+9wx3XzDeu8ws0GL/JnPpBuyYFkmSRscrgNcl2Q1cD/xUkvcAj7SuvbT3/W39PcCJXduvBva29NXTpEuStCCzClYd0yJJ0mipqsuranVVnUTnIvNfVtUbgR3AxrbaRuCGtrwD2JDkqCQn07nofEfrMvxYkrPbLMAXdm0jSdK8zfb+89vo45gWSZK0ZK4Atie5GHgIOB+gqu5Jsh24FzgIXFJVT7Rt3sSTw3xuai9JkhbksMHqUoxp6fUEDKMyqLyfrLO5s87mbtTqrNcTMExntpMyjFK9avFV1QQw0Za/Bpwzw3pbgC3TpN8JnLZ4OZQkLUezubM6OablZ4BnAcd0j2lpd1V7Oqal1xMwjMqg8n6yzubOOpu7UauzXk/AMJ3ZTsowSJMvSJIkzcdhx6w6pkWSJEmS1G8Lec6qY1okSZIkSYtiTsGqY1okSZIkSf0wl+esSpIkSZLUFwarkiRJkqSBY7AqSZIkSRo4BquSJEmSpIFjsCpJkiRJGjgGq5IkSZKkgWOwKkmSJEkaOAarkiRJkqSBY7AqSZIkSRo4BquSJEmSpIFjsCpJ0jKV5FlJ7kjyd0nuSfK7Lf34JDcneaC9H9e1zeVJdiW5P8m5XelnJtnZvrsqSZaiTJKk0WGwKknS8vU48FNV9TLgDGBdkrOBzcAtVbUGuKV9JskpwAbgVGAd8M4kR7R9XQ1sAta017o+lkOSNIIMViVJWqaq40D7+Iz2KmA9sK2lbwPOa8vrgeur6vGqehDYBZyVZCVwTFXdWlUFXNu1jSRJ83LYYNUuQpIkja4kRyS5G9gP3FxVtwNjVbUPoL2f0FZfBTzctfmelraqLU9NlyRp3o6cxTqTXYQOJHkG8IkkNwH/G50uQlck2Uyni9BbpnQRehHwF0l+oKqe4MkuQrcBH6HTReimnpdKkiTNSmufz0hyLPChJKcdYvXpLjLXIdKfunGyic55AGNjY0xMTMw5v1MdOHCgJ/tZaqNSDhidsoxKOWB0yrJU5bjs9IM93d/YioXtc5B+y8X+TQ4brLbuPDN1ERpv6duACeAtdHURAh5MMtlFaDetixBAkskuQgarkiQtsap6NMkEnQvJjyRZWVX7Whff/W21PcCJXZutBva29NXTpE89xlZgK8DatWtrfHx8wfmemJigF/tZaqNSDhidsoxKOWB0yrJU5bho84093d9lpx/kyp2zuWc4vd0XjPcuMwu02L/JrMas2kVIkqTRk+SF7Y4qSVYArwQ+B+wANrbVNgI3tOUdwIYkRyU5mc5ESne084DHkpzdhvhc2LWNJEnzMquQvp9dhKD33YRGpetDP1lnc2edzd2o1VmvuwlNZ7Zdh0apXrWoVgLb2oy+3wNsr6oPJ7kV2J7kYuAh4HyAqronyXbgXuAgcEk7RwB4E3ANsIJOryl7TkmSFmRO95/70UWoHaen3YRGpetDP1lnc2edzd2o1VmvuwlNZ7Zdhwapi5AGV1V9BvjhadK/BpwzwzZbgC3TpN8JHOpitiRJczKb2YDtIiRJkiRJ6qvZ3Fm1i5AkSZIkqa9mMxuwXYQkSZIkSX01q9mAJUmSJEnqJ4NVSZIkSdLAMViVJEmSJA0cg1VJkiRJ0sAxWJUkSZIkDRyDVUmSJEnSwDFYlSRJkiQNHINVSZIkSdLAMViVJEmSJA0cg1VJkiRJ0sAxWJUkSZIkDRyDVUmSJEnSwDFYlSRpmUpyYpL/keS+JPckubSlH5/k5iQPtPfjura5PMmuJPcnObcr/cwkO9t3VyXJUpRJkjQ6DFYlSVq+DgKXVdUPAWcDlyQ5BdgM3FJVa4Bb2mfadxuAU4F1wDuTHNH2dTWwCVjTXuv6WRBJ0ug5bLDqVVdJkkZTVe2rqk+15ceA+4BVwHpgW1ttG3BeW14PXF9Vj1fVg8Au4KwkK4FjqurWqirg2q5tJEmal9ncWfWqqyRJIy7JScAPA7cDY1W1DzoBLXBCW20V8HDXZnta2qq2PDVdkqR5O/JwK7RGarLBeixJ91XX8bbaNmACeAtdV12BB5NMXnXdTbvqCpBk8qrrTb0rjiRJmqskzwH+HPiVqvrmITo+TfdFHSJ96nE20blozdjYGBMTE/PKb7cDBw70ZD9LbVTKAaNTllEpB4xOWZaqHJedfrCn+xtbsbB9DtJvudi/yWGD1W6HuuqapPuq621dm01eXf0OXnWVJGmgJHkGnUD1uqr6YEt+JMnK1r6vBPa39D3AiV2brwb2tvTV06Q/RVVtBbYCrF27tsbHxxec/4mJCXqxn6U2KuWA0SnLqJQDRqcsS1WOizbf2NP9XXb6Qa7cOacw7Cl2XzDeu8ws0GL/JrOupX5ddW3H6umV11G5mtRP1tncWWdzN2p11usrr9OZ7dXYUapXLZ42d8S7gfuq6g+6vtoBbASuaO83dKW/N8kfAC+iM6Tnjqp6IsljSc6mc0H7QuAdfSqGJGlEzSpY7edVV+j9lddRuZrUT9bZ3FlnczdqddbrK6/Tme3V2EG66qqB9grg54GdSe5uab9BJ0jdnuRi4CHgfICquifJduBeOnNaXFJVT7Tt3gRcA6ygM8THYT6SpAU57BmPV10lSRpNVfUJpu/5BHDODNtsAbZMk34ncFrvcidJWu5mc2fVq66SJEmSpL6azWzAXnWVJEmSJPXVbJ6zKkmSJElSXxmsSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpK0TCX5kyT7k3y2K+34JDcneaC9H9f13eVJdiW5P8m5XelnJtnZvrsqSfpdFknS6DlssGpDJknSyLoGWDclbTNwS1WtAW5pn0lyCrABOLVt884kR7RtrgY2AWvaa+o+JUmas9ncWb0GGzJJkkZOVf018PdTktcD29ryNuC8rvTrq+rxqnoQ2AWclWQlcExV3VpVBVzbtY0kSfN25OFWqKq/TnLSlOT1wHhb3gZMAG+hqyEDHkwy2ZDtpjVkAEkmG7KbFlwCSZLUS2NVtQ+gqvYlOaGlrwJu61pvT0v7Tluemv40STbRuXDN2NgYExMTC87sgQMHerKfpTYq5YDRKcuolANGpyxLVY7LTj/Y0/2NrVjYPgfpt1zs3+SwweoMFq0hg943ZqPyH7SfrLO5s87mbtTqrNeN2XRm28CNUr1qYEw3fKcOkf70xKqtwFaAtWvX1vj4+IIzNTExQS/2s9RGpRwwOmUZlXLA6JRlqcpx0eYbe7q/y04/yJU75xuGwe4LxnuXmQVa7N9k/rU0vQU3ZND7xmxU/oP2k3U2d9bZ3I1anfW6MZvObBu4QWrINHQeSbKyXYxeCexv6XuAE7vWWw3sbemrp0mXJGlB5jsb8COtAcOGTJKkkbID2NiWNwI3dKVvSHJUkpPpzD9xR+tp9ViSs9vkiRd2bSNJ0rzNN1i1IZMkacgleR9wK/DSJHuSXAxcAbwqyQPAq9pnquoeYDtwL/BR4JKqeqLt6k3Au+hMuvR5nJNCktQDh+1L1hqyceAFSfYAb6XTcG1vjdpDwPnQaciSTDZkB3l6Q3YNsIJOI2ZDJknSEqqqN8zw1TkzrL8F2DJN+p3AaT3MmiRJs5oN2IZMkiRJktRX8+0GLEmSJEnSojFYlSRJkiQNHINVSZIkSdLAMViVJEmSJA0cg1VJkiRJ0sA57GzAkiRJg2rnl77BRZtvXOpsALD7itcsdRYkaaQsi2B1kBoysDGTJEmSpMOxG7AkSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgZO34PVJOuS3J9kV5LN/T6+JElaHLbxkqRe6utzVpMcAfwR8CpgD/DJJDuq6t5+5kOSRt1JPltafWYbr0Pxb9JwGKTfaTqXnX6QiwY8j+qtvgarwFnArqr6AkCS64H1gA2ZpDkb9EZVWmZs46U56lc7ZpCnYdXvYHUV8HDX5z3Aj/Q5D5Ikqfds4zUUZhsgGuBJS6/fwWqmSaunrZRsAja1jweS3L/A474A+OoC99Ez+b2lzsGsDFSdDQnrbO6sszl68xDWWQ//5r24Z3vSYjhsG78I7TsM0P+JBf5bH5hy9MBIlGUY/97OZFTKYjk6BiyW6NVvMm0b3+9gdQ9wYtfn1cDeqStV1VZga68OmuTOqlrbq/0tB9bZ3Flnc2edzZ11pgF22Da+1+07jM7/iVEpB4xOWUalHDA6ZbEcg2exy9Lv2YA/CaxJcnKSZwIbgB19zoMkSeo923hJUk/19c5qVR1M8kvAx4AjgD+pqnv6mQdJktR7tvGSpF7rdzdgquojwEf6fNiedjlaJqyzubPO5s46mzvrTAPLNn5BRqUcMDplGZVywOiUxXIMnkUtS6qeNr+RJEmSJElLqt9jViVJkiRJOqyRD1aTrEtyf5JdSTYvdX4GUZITk/yPJPcluSfJpS39+CQ3J3mgvR+31HkdJEmOSPLpJB9un62vQ0hybJIPJPlc+7f2o9bZoSX51fZ/8rNJ3pfkWdaZ1DFK7XuS3Ul2Jrk7yZ1LnZ+5SPInSfYn+WxX2tD9nZqhHL+T5Evtd7k7yc8sZR5nY1TO6Q5RjmH8TZ6V5I4kf9fK8rstfdh+k5nKsai/yUh3A05yBPA/gVfRmVL/k8AbqureJc3YgEmyElhZVZ9K8lzgLuA84CLg76vqinYicFxVvWXpcjpYkvwasBY4pqpem+Q/Yn3NKMk24G+q6l1tptBnA7+BdTatJKuATwCnVNU/JtlOZyzgKVhnWuZGrX1PshtYW1VD9/zIJD8OHACurarTWtrQtYczlON3gANV9ftLmbe5GJVzukOU4/UM328S4OiqOpDkGXTa9kuB/43h+k1mKsc6FvE3GfU7q2cBu6rqC1X1beB6YP0S52ngVNW+qvpUW34MuA9YRaeutrXVttH5IyEgyWrgNcC7upKtrxkkOQb4ceDdAFX17ap6FOvscI4EViQ5kk5wvxfrTALb94FRVX8N/P2U5KH7OzVDOYbOqJzTHaIcQ6c6DrSPz2ivYvh+k5nKsahGPVhdBTzc9XkPQ/oPvV+SnAT8MHA7MFZV+6DzRwM4YQmzNmjeBvw68E9dadbXzF4CfAX403S6Tr8rydFYZzOqqi8Bvw88BOwDvlFVH8c6k2D02vcCPp7kriSbljozPTBKf6d+KclnWjfhge6mOdWonNNNKQcM4W+SztCxu4H9wM1VNZS/yQzlgEX8TUY9WM00aaPb73mBkjwH+HPgV6rqm0udn0GV5LXA/qq6a6nzMkSOBF4OXF1VPwx8CxjqMWaLrf2xXw+cDLwIODrJG5c2V9LAGLX2/RVV9XLg1cAlrUuqlt7VwD8DzqBz0fDKJc3NHIzKOd005RjK36SqnqiqM4DVwFlJTlviLM3LDOVY1N9k1IPVPcCJXZ9X0+lGpyla3/M/B66rqg+25EfamIHJsQP7lyp/A+YVwOvaGKPrgZ9K8h6sr0PZA+zpugL3ATrBq3U2s1cCD1bVV6rqO8AHgR/DOpNgxNr3qtrb3vcDH6LTzXmYjcTfqap6pJ2c/xPwXxiS32VUzummK8ew/iaT2hCoCTrjPIfuN5nUXY7F/k1GPVj9JLAmycltQpcNwI4lztPAaQOm3w3cV1V/0PXVDmBjW94I3NDvvA2iqrq8qlZX1Ul0/k39ZVW9EetrRlX1ZeDhJC9tSecA92KdHcpDwNlJnt3+j55DZ8yOdSaNUPue5Og2gQxteMRPA5899FYDbyT+Tk0GEs3/yhD8LqNyTjdTOYb0N3lhkmPb8go6F6M/x/D9JtOWY7F/k5GeDRigTZ/8NuAI4E+qasvS5mjwJPkXwN8AO3lyDOZv0BkbsB34PjonzudX1dBPPtBLScaB/7vNBvx8rK8ZJTmDzoRUzwS+APwCnQtm1tkM2rTw/xo4CHwa+DfAc7DOpJFp35O8hM7dVOgMmXjvMJUlyfuAceAFwCPAW4H/xpD9nZqhHON0ujYWsBv4xckxhoNqVM7pDlGONzB8v8k/pzOB0hG0856q+nfDdt54iHL8VxbxNxn5YFWSJEmSNHxGvRuwJEmSJGkIGaxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJIyTJS5N8OsljSd681PmR5stgVVpkST6W5N9Nk74+yZeTHDnDduNJ9ix+DiVJGgxJJpJ8PclRS52XXkjyA0n+LMlXk3wjyWeS/FqSIxawz99J8p7DrPbrwERVPbeqrprvseZ4TKnnDFalxXcN8PNJMiX954Hrqupg/7MkSdJgSXIS8C+BAl63CPuf9uLwYknyz4DbgYeB06vqecD5wFrguYt8+BcD98xnw37Xk3QoBqvS4vtvwPF0GmAAkhwHvBa4Nsnbkuxtr7clOSrJ0cBNwIuSHGivFyX5niSbk3w+ydeSbE9yfNvns5K8p6U/muSTScaWoLySJM3HhcBtdC7ybgRobeKjSU6bXCnJC5P8Y5IT2ufXJrm7rfe3Sf5517q7k7wlyWeAbyU5sqsdfSzJvUn+1671j0hyZbsT+mCSX0pSkwFckucleXeSfUm+lOTfH+Iu6e8Cf1tVv1ZV+wCq6v6q+rmqerTt73VJ7ml5n0jyQ115eUs7xmNJ7k9yTpJ1wG8A/7qdG/zd1IMm+UvgJ4E/bOv8QMv3tUm+kuSLSX4ryfe09S9K8v8k+U9J/h74nSn7e9oxk/xkkp1d6/xFkju6Pn8iyXlt+Yda2R5tZe35hQiNLoNVaZFV1T8C2+k0wpNeD3wO+N+Bs4EzgJcBZwG/VVXfAl4N7K2q57TXXuDNwHnATwAvAr4O/FHb50bgecCJwPOB/xP4x8UsmyRJPXQhcF17nZtkrKoeBz4IvKFrvdcDf1VV+5O8HPgT4BfptH1/DOyY0o34DcBrgGNbb6bP07mA/Dw6AeV7kqxs6/5bOu3vGcDL6bS53bYBB4HvB34Y+Gng38xQnlcCH5ipsEl+AHgf8CvAC4GPAP89yTOTvBT4JeB/qarnAucCu6vqo8B/AN7fzg1eNnW/VfVTwN8Av9TW+Z/AO1p5X0LnHOJC4Be6NvsR4AvACcCWKfub7pi3At+f5AUtkD8NWJ3kuUlWAGcCf5PkGcB/Bz7e9v3LwHWtfNJhGaxK/bENOL/9AYdOI7ENuAD4d1W1v6q+QqfR/PlD7OcXgd+sqj2tAf8d4F+1huI7dBrq76+qJ6rqrqr65iKVR5KknknyL+h0Xd1eVXfRCSh/rn39Xp4arP5cS4NOcPnHVXV7a/u2AY/TuRA86aqqerhdPKaq/qyq9lbVP1XV+4EH6Fwshk4g/PbWzn4duKIrj2N0AtlfqapvVdV+4D8BG2Yo1vOBfYco9r8Gbqyqm6vqO8DvAyuAHwOeAI4CTknyjKraXVWfP8S+ZtTu/P5r4PKqeqyqdgNX8tTzjb1V9Y6qOjhZT4dSVf8vcCfw43S6NX8G+ATwCjp1/0BVfa0tPwe4oqq+XVV/CXyYp/6e0owMVqU+qKpPAF8B1id5CfC/0GloXwR8sWvVL7a0mbwY+FDrSvMocB+dBm0M+K/Ax4DrW5fi/9iuaEqSNOg2Ah+vqq+2z+9taQB/CaxI8iNJXkznrueH2ncvBi6bbBdb23giT21LH+4+UJILu7oNP0rnruAL2tcvmrJ+9/KLgWcA+7q2/WM6dwyn8zVg5QzfTR7ru+cAVfVP7XirqmoXnTuuvwPsT3J9kkOdHxzKC4Bn8vTzjVVdn59SR7P0V8A4nYD1r4AJOndtf6J9hlafrWwzHVuakcGq1D/X0rmj+vN0GuRHgL10Gr9J39fSoDPBxFQPA6+uqmO7Xs+qqi9V1Xeq6ner6hQ6V2Vfy1O7HkuSNHBar6PXAz+Rziz5XwZ+FXhZkpe1QGc7nbtxPwd8uKoea5s/DGyZ0i4+u6re13WI6jrWi4H/QqeL7fOr6ljgs8DkJIj7gNVd257Ytfwwnbu2L+g61jFVdeoMRfsLOsN9ZvKUc4Akacf7EkBVvbeqJu84F/B7U8szS1+l0/tq6vnGl7o+H26f030/NVj9K54erO4FTpwcHzvDsaUZGaxK/XMtnfEr/5ZOF2DojFX5rTZZxAuA3wYmp4Z/BHh+kud17eM/A1taYzs5ycT6tvyTSU5v3X2+SadhemKxCyVJ0gKdR6e9OoXOXdMzgB+iM+5y8qLre+l0Zb2AJ7sAQyfw/D/bXdckOTrJa5LMNNvu0XQCr68AJPkFOndWJ20HLk2yKsmxwFsmv2iTJH0cuDLJMelMevjPkvzEDMd6K/BjSf5/Sb63He/705kM8dh2rNe0iZOeAVxGJxj+23Sek/pTbezt/0tnDorJNv0R4KQpAeCMquqJdqwtbUzpi4Ff48nzjdmY7ph/C7yUThfqO6rqHjoB8Y8Af93WuR34FvDrSZ6RZBz4WeD6ORxby5jBqtQnbYzI39JpKHe05H9PZ8zHZ4CdwKdaGlX1OTrB7Bdad6MXAW9v2348yWN0Zk38kbav76UzkcM36XQP/ivm1hBJkrQUNgJ/WlUPVdWXJ1/AHwIXJDmyqiaDnhfRmS0fgKq6k85F4D+kM+ngLuCimQ5UVffSGa95K50A7HTg/+la5b/QCUg/A3yazqRHB3kyULyQTpfae9vxPsAMXX3bGNMfBU4C7knyDeDP6bT7j1XV/cAb6Ux+9FU6QdzPVtW36YxXvaKlf5lOV+PfaLv+s/b+tSSfmqmsU/wynfr7Ap2xpe+lMzHVbD3tmG0yyE8B97Q8Q6dev9jG89LSX0dnrO9XgXcCF7ZzHOmwUjXXngSSJEnS6EvyauA/V9WLD7uypJ7zzqokSZJEZ/xskp9J53msq+h05f3Q4baTtDi8sypJkiQBSZ5NZxjND9IZJ3ojcKmPgpOWhsGqJEmSJGng2A1YkiRJkjRwDFYlSZIkSQPnyKXOwOG84AUvqJNOOmlB+/jWt77F0Ucf3ZsMDanlXgfLvfxgHYB1MOzlv+uuu75aVS9c6nyoNxbavg/bv+dhyy8MX57N7+Ibtjyb38XXqzzP1MYPfLB60kknceeddy5oHxMTE4yPj/cmQ0NqudfBci8/WAdgHQx7+ZN8canzoN5ZaPs+bP+ehy2/MHx5Nr+Lb9jybH4XX6/yPFMbbzdgSZIkSdLAMViVJEmSJA0cg1VJkiRJ0sAxWJUkSZIkDRyDVUmSJEnSwDFYlSRpmUry0iR3d72+meRXkhyf5OYkD7T347q2uTzJriT3Jzm3K/3MJDvbd1clydKUSpI0KgxWJUlapqrq/qo6o6rOAM4E/gH4ELAZuKWq1gC3tM8kOQXYAJwKrAPemeSItrurgU3AmvZa18eiSJJG0MA/Z7UXdn7pG1y0+calzsZ37b7iNUudBUmSpjoH+HxVfTHJemC8pW8DJoC3AOuB66vqceDBJLuAs5LsBo6pqlsBklwLnAfctNiZPsn2XZJG1rIIViVJ0mFtAN7Xlseqah9AVe1LckJLXwXc1rXNnpb2nbY8Nf0pkmyic/eVsbExJiYm5p3ZAwcOMDExwWWnH5z3PnrtUOWZzO8wGbY8m9/FN2x5Nr+Lb7HzbLAqSdIyl+SZwOuAyw+36jRpdYj0pyZUbQW2Aqxdu7bGx8fnltEuExMTjI+PD1bPqQvGZ/xuMr/DZNjybH4X37Dl2fwuvsXOs2NWJUnSq4FPVdUj7fMjSVYCtPf9LX0PcGLXdquBvS199TTpkiTNm8GqJEl6A092AQbYAWxsyxuBG7rSNyQ5KsnJdCZSuqN1GX4sydltFuALu7aRJGleZhWsJvnVJPck+WyS9yV5ltPaS5I0/JI8G3gV8MGu5CuAVyV5oH13BUBV3QNsB+4FPgpcUlVPtG3eBLwL2AV8nj5MriRJGm2HHbOaZBXwZuCUqvrHJNvpTMJwCp1p7a9IspnOtPZvmTKt/YuAv0jyA60xm5zW/jbgI3SmtbcxkyRpiVTVPwDPn5L2NTqzA0+3/hZgyzTpdwKnLUYeJUnL02y7AR8JrEhyJPBsOuNQ1tOZzp72fl5b/u609lX1IJ0rrGe1MS/HVNWtVVXAtV3bSJIkSZL0XYcNVqvqS8DvAw8B+4BvVNXHmTKtPdA9rf3DXbuYnL5+FbOY1l6SJEmSpNl0Az6Ozt3Sk4FHgT9L8sZDbTJN2qyntW/H7Nlz2ADGVjA0z2FbLMP43KZeWu7lB+sArIPlXn5JkjRcZvOc1VcCD1bVVwCSfBD4Mdq09u1h4T2d1r6Xz2EDeMd1N3DlzsF5pOyhnsO2WIbxuU29tNzLD9YBWAfLvfySJGm4zGbM6kPA2Ume3WbvPQe4D6e1lyRJkiQtksPebqyq25N8APgUcBD4NJ27ns8Btie5mE5Ae35b/542Y/C9bf2p09pfA6ygMwuwMwFLkiRJkp5mVn1jq+qtwFunJD+O09pLkiRJkhbBbB9dI0mSJElS3xisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkrSMJTk2yQeSfC7JfUl+NMnxSW5O8kB7P65r/cuT7Epyf5Jzu9LPTLKzfXdVkixNiSRJo8JgVZKk5e3twEer6geBlwH3AZuBW6pqDXBL+0ySU4ANwKnAOuCdSY5o+7ka2ASsaa91/SyEJGn0GKxKkrRMJTkG+HHg3QBV9e2qehRYD2xrq20DzmvL64Hrq+rxqnoQ2AWclWQlcExV3VpVBVzbtY0kSfNisCpJ0vL1EuArwJ8m+XSSdyU5Ghirqn0A7f2Etv4q4OGu7fe0tFVteWq6JEnzduThVkjyUuD9XUkvAX6bzlXT9wMnAbuB11fV19s2lwMXA08Ab66qj7X0M4FrgBXAR4BL2xVYSZLUf0cCLwd+uapuT/J2WpffGUw3DrUOkf7UjZNNdLoKMzY2xsTExJwzPOnAgQNMTExw2ekH572PXjtUeSbzO0yGLc/md/ENW57N7+Jb7DwfNlitqvuBMwDauJQvAR/iyfEsVyTZ3D6/Zcp4lhcBf5HkB6rqCZ4cz3IbnWB1HXBTrwslSZJmZQ+wp6pub58/QKc9fyTJyqra17r47u9a/8Su7VcDe1v66mnSn6KqtgJbAdauXVvj4+PzzvjExATj4+NctPnGee+j13ZfMD7jd5P5HSbDlmfzu/iGLc/md/Etdp7n2g34HODzVfVFHM8iSdJQq6ovAw+3XlTQaefvBXYAG1vaRuCGtrwD2JDkqCQn05lI6Y7WVfixJGe3WYAv7NpGkqR5Oeyd1Sk2AO9ry08Zz5KkezzLbV3bTI5b+Q6zHM/Sy25CAGMrGJpuQotlGLsV9NJyLz9YB2AdLPfya0a/DFyX5JnAF4BfoHMxe3uSi4GHgPMBquqeJNvpBLQHgUtazymAN/HkUJ+bsOeUJGmBZh2stkbsdcDlh1t1mrRZj2eB3nYTAnjHdTdw5c65xuWL51DdhBbLMHYr6KXlXn6wDsA6WO7l1/Sq6m5g7TRfnTPD+luALdOk3wmc1tPMSZKWtbl0A3418KmqeqR9fqR17aWX41kkSZIkSZpLsPoGnuwCDI5nkSRJkiQtkln1jU3ybOBVwC92JV+B41kkSZIkSYtgVsFqVf0D8PwpaV/D8SySJEmSpEUw10fXSJIkSZK06AxWJUmSJEkDx2BVkiRJkjRwDFYlSZIkSQPHYFWSJEmSNHAMViVJkiRJA8dgVZIkSZI0cAxWJUmSJEkDx2BVkiRJkjRwDFYlSZIkSQPHYFWSJEmSNHAMViVJkiRJA2dWwWqSY5N8IMnnktyX5EeTHJ/k5iQPtPfjuta/PMmuJPcnObcr/cwkO9t3VyXJYhRKkiRJkjTcZntn9e3AR6vqB4GXAfcBm4FbqmoNcEv7TJJTgA3AqcA64J1Jjmj7uRrYBKxpr3U9KockSZqHJLvbheS7k9zZ0rwgLUlacocNVpMcA/w48G6Aqvp2VT0KrAe2tdW2Aee15fXA9VX1eFU9COwCzkqyEjimqm6tqgKu7dpGkiQtnZ+sqjOqam377AVpSdKSm82d1ZcAXwH+NMmnk7wrydHAWFXtA2jvJ7T1VwEPd22/p6WtastT0yVJ0mDxgrQkacml06YcYoVkLXAb8Iqquj3J24FvAr9cVcd2rff1qjouyR8Bt1bVe1r6u4GPAA8B/9+qemVL/5fAr1fVz05zzE10rs4yNjZ25vXXX7+gQu7/+2/wyD8uaBc9dfqq5/X9mAcOHOA5z3lO3487KJZ7+cE6AOtg2Mv/kz/5k3d13flTjyR5EPg6UMAfV9XWJI/O0Mb/IXDblDb+JmA3cMWUNv4tVfXaKcfqWfs++e9555e+Me999Nqh2vdh/P83bHk2v4tv2PJsfhdfr/I8Uxt/5Cy23QPsqarb2+cP0OkO9EiSlVW1r11R3d+1/old268G9rb01dOkP01VbQW2Aqxdu7bGx8dnkc2ZveO6G7hy52yK2h+7Lxjv+zEnJiZYaD0Os+VefrAOwDpY7uXXjF5RVXuTnADcnORzh1h3unGodYj0pyb0sH2f/Pd80eYb572PXjtU+z6M//+GLc/md/ENW57N7+Jb7DwfthtwVX0ZeDjJS1vSOcC9wA5gY0vbCNzQlncAG5IcleRkOuNW7mhdhR9LcnabdOHCrm0kSdISqKq97X0/8CHgLNoFaYBeX5CWJGm2Zjsb8C8D1yX5DHAG8B+AK4BXJXkAeFX7TFXdA2ynE9B+FLikqp5o+3kT8C46Y1w+T6frkCRJWgJJjk7y3Mll4KeBz+IFaUnSAJhV39iquhuYbpzQOTOsvwXYMk36ncBpc8ifJElaPGPAh9pTZo4E3ltVH03ySWB7kovpzDlxPnQuSCeZvCB9kKdfkL4GWEHnYrQXpCVJCzI4AzklSVJfVdUX6Dw/fWr61/CCtCRpic22G7AkSZIkSX1jsCpJkiRJGjgGq5IkSZKkgWOwKkmSJEkaOAarkiRJkqSBY7AqSZIkSRo4BquSJEmSpIFjsCpJkiRJGjgGq5IkSZKkgWOwKkmSJEkaOAarkiRJkqSBM6tgNcnuJDuT3J3kzpZ2fJKbkzzQ3o/rWv/yJLuS3J/k3K70M9t+diW5Kkl6XyRJkiRJ0rCby53Vn6yqM6pqbfu8GbilqtYAt7TPJDkF2ACcCqwD3pnkiLbN1cAmYE17rVt4ESRJkiRJo2Yh3YDXA9va8jbgvK7066vq8ap6ENgFnJVkJXBMVd1aVQVc27WNJEmSJEnfNdtgtYCPJ7kryaaWNlZV+wDa+wktfRXwcNe2e1raqrY8NV2SJEmSpKc4cpbrvaKq9iY5Abg5yecOse5041DrEOlP30EnIN4EMDY2xsTExCyzOb2xFXDZ6QcXtI9eWmh55uPAgQNLctxBsdzLD9YBWAfLvfySJGm4zCpYraq97X1/kg8BZwGPJFlZVftaF9/9bfU9wIldm68G9rb01dOkT3e8rcBWgLVr19b4+PisCzSdd1x3A1funG1cvvh2XzDe92NOTEyw0HocZsu9/GAdgHWw3Muv6bV5Je4EvlRVr01yPPB+4CRgN/D6qvp6W/dy4GLgCeDNVfWxln4mcA2wAvgIcGkb8iNJ0rwdthtwkqOTPHdyGfhp4LPADmBjW20jcENb3gFsSHJUkpPpTKR0R+sq/FiSs9sswBd2bSNJkpbGpcB9XZ+dQFGSNBBmM2Z1DPhEkr8D7gBurKqPAlcAr0ryAPCq9pmqugfYDtwLfBS4pKqeaPt6E/AuOpMufR64qYdlkSRJc5BkNfAaOm3zJCdQlCQNhMP2ja2qLwAvmyb9a8A5M2yzBdgyTfqdwGlzz6YkSVoEbwN+HXhuV9pTJlBs81VAZ1LE27rWm5wo8Ts4gaIkaREMzkBOSZLUN0leC+yvqruSjM9mk2nSlmwCxckJw4ZlAsVhnOBs2PJsfhffsOXZ/C6+xc6zwaokScvTK4DXJfkZ4FnAMUnew5BMoDg5YdhFm2+c9z567VATKA7jBGfDlmfzu/iGLc/md/Etdp5n+5xVSZI0Qqrq8qpaXVUn0Zk46S+r6o04gaIkaUB4Z1WSJHW7Atie5GLgIeB86EygmGRyAsWDPH0CxWvoPLrmJpxAUZLUAwarkiQtc1U1AUy0ZSdQlCQNBLsBS5IkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngzDpYTXJEkk8n+XD7fHySm5M80N6P61r38iS7ktyf5Nyu9DOT7GzfXZUkvS2OJEmSJGkUzOXO6qXAfV2fNwO3VNUa4Jb2mSSnABuAU4F1wDuTHNG2uRrYBKxpr3ULyr0kSZIkaSTNKlhNshp4DfCuruT1wLa2vA04ryv9+qp6vKoeBHYBZyVZCRxTVbdWVQHXdm0jSZIkSdJ3HTnL9d4G/Drw3K60saraB1BV+5Kc0NJXAbd1rbenpX2nLU9Nf5okm+jcgWVsbIyJiYlZZnN6YyvgstMPLmgfvbTQ8szHgQMHluS4g2K5lx+sA7AOlnv5JUnScDlssJrktcD+qroryfgs9jndONQ6RPrTE6u2AlsB1q5dW+PjsznszN5x3Q1cuXO2cfni233BeN+POTExwULrcZgt9/KDdQDWwXIvvyRJGi6zieBeAbwuyc8AzwKOSfIe4JEkK9td1ZXA/rb+HuDEru1XA3tb+upp0iVJkiRJeorDjlmtqsuranVVnURn4qS/rKo3AjuAjW21jcANbXkHsCHJUUlOpjOR0h2ty/BjSc5uswBf2LWNJEmSJEnftZC+sVcA25NcDDwEnA9QVfck2Q7cCxwELqmqJ9o2bwKuAVYAN7WXJEmSJElPMadgtaomgIm2/DXgnBnW2wJsmSb9TuC0uWZSkiRJkrS8zOU5q5IkaYQkeVaSO5L8XZJ7kvxuSz8+yc1JHmjvx3Vtc3mSXUnuT3JuV/qZSXa2765qQ34kSZo3g1VJkpavx4GfqqqXAWcA65KcDWwGbqmqNcAt7TNJTqEzf8WpwDrgnUmOaPu6ms5j59a017o+lkOSNIIMViVJWqaq40D7+Iz2KmA9sK2lbwPOa8vrgeur6vGqehDYBZzVngpwTFXdWlUFXNu1jSRJ8zI4Dx+VJEl91+6M3gV8P/BHVXV7krE2iz/tEXUntNVXAbd1bb6npX2nLU9Nn3qsTXTuvjI2NsbExMS8833gwAEmJia47PSD895Hrx2qPJP5HSbDlmfzu/iGLc/md/Etdp4NViVJWsbajP1nJDkW+FCSQ02EON041DpE+tRjbQW2Aqxdu7bGx8fnnN9JExMTjI+Pc9HmG+e9j17bfcH4jN9N5neYDFueze/iG7Y8m9/Ft9h5thuwJEmiqh6lM+P/OuCR1rWX9r6/rbYHOLFrs9XA3pa+epp0SZLmzWBVkqRlKskL2x1VkqwAXgl8DtgBbGyrbQRuaMs7gA1JjkpyMp2JlO5oXYYfS3J2mwX4wq5tJEmaF7sBS5K0fK0EtrVxq98DbK+qDye5Fdie5GLgIeB8gKq6J8l24F7gIHBJ60YM8CbgGmAFcFN7SZI0bwarkiQtU1X1GeCHp0n/GnDODNtsAbZMk34ncKjxrpIkzYndgCVJkiRJA8dgVZIkSZI0cAxWJUmSJEkD57DBapJnJbkjyd8luSfJ77b045PcnOSB9n5c1zaXJ9mV5P4k53aln5lkZ/vuqjZjoCRJkiRJTzGbO6uPAz9VVS8DzgDWJTkb2AzcUlVrgFvaZ5KcAmwATqXzrLZ3tlkGAa4GNtGZ6n5N+16SJEmSpKc4bLBaHQfax2e0VwHrgW0tfRtwXlteD1xfVY9X1YPALuCs9lDxY6rq1qoq4NqubSRJkiRJ+q5ZjVlNckSSu4H9wM1VdTsw1h4CTns/oa2+Cni4a/M9LW1VW56aLkmSJEnSU8zqOavtgd9nJDkW+FCSQz1HbbpxqHWI9KfvINlEp7swY2NjTExMzCabMxpbAZedfnBB++ilhZZnPg4cOLAkxx0Uy738YB2AdbDcyy9JkobLrILVSVX1aJIJOmNNH0mysqr2tS6++9tqe4ATuzZbDext6aunSZ/uOFuBrQBr166t8fHxuWTzad5x3Q1cuXNORV1Uuy8Y7/sxJyYmWGg9DrPlXn6wDsA6WO7llyRJw2U2swG/sN1RJckK4JXA54AdwMa22kbghra8A9iQ5KgkJ9OZSOmO1lX4sSRnt1mAL+zaRpIkSZKk75rN7caVwLY2o+/3ANur6sNJbgW2J7kYeAg4H6Cq7kmyHbgXOAhc0roRA7wJuAZYAdzUXpIkSZIkPcVhg9Wq+gzww9Okfw04Z4ZttgBbpkm/EzjUeFdJkiRJkmY3G7AkSZIkSf1ksCpJkiRJGjgGq5IkSZKkgWOwKkmSJEkaOAarkiQtU0lOTPI/ktyX5J4kl7b045PcnOSB9n5c1zaXJ9mV5P4k53aln5lkZ/vuqvaYOkmS5s1gVZKk5esgcFlV/RBwNnBJklOAzcAtVbUGuKV9pn23ATgVWAe8sz3aDuBqYBOd56uvad9LkjRvBquSJC1TVbWvqj7Vlh8D7gNWAeuBbW21bcB5bXk9cH1VPV5VDwK7gLOSrASOqapbq6qAa7u2kSRpXgxWJUkSSU6i81z124GxqtoHnYAWOKGttgp4uGuzPS1tVVuemi5J0rwdudQZkCRJSyvJc4A/B36lqr55iOGm031Rh0ifepxNdLoKMzY2xsTExLzyC3DgwAEmJia47PSD895Hrx2qPJP5HSbDlmfzu/iGLc/md/Etdp4NViVJWsaSPINOoHpdVX2wJT+SZGVV7WtdfPe39D3AiV2brwb2tvTV06Q/RVVtBbYCrF27tsbHx+ed74mJCcbHx7lo843z3kev7b5gfMbvJvM7TIYtz+Z38Q1bns3v4lvsPNsNWJKkZarN2Ptu4L6q+oOur3YAG9vyRuCGrvQNSY5KcjKdiZTuaF2FH0tydtvnhV3bSJI0L95ZlSRp+XoF8PPAziR3t7TfAK4Atie5GHgIOB+gqu5Jsh24l85MwpdU1RNtuzcB1wArgJvaS5KkeTtssJrkRDqz+n0v8E/A1qp6e5LjgfcDJwG7gddX1dfbNpcDFwNPAG+uqo+19DN5siH7CHBpmzVQkiT1WVV9gunHmwKcM8M2W4At06TfCZzWu9xJkpa72XQD9hlskiRJkqS+Omyw6jPYJEmSJEn9NqcJlnwGmyRJkiSpH2Y9wVK/nsHWjtWz57ABjK1gaJ7DtliG8blNvbTcyw/WAVgHy738kiRpuMwqWO3nM9igt89hA3jHdTdw5c7Bmfj4UM9hWyzD+NymXlru5QfrAKyD5V5+SZI0XA7bDdhnsEmSJEmS+m02txt9BpskSZIkqa8OG6z6DDZJkiRJUr/NaTZgSZIkSZL6wWBVkiRJkjRwDFYlSZIkSQPHYFWSJEmSNHAMViVJkiRJA8dgVZIkSZI0cAxWJUmSJEkDx2BVkiRJkjRwDFYlSZIkSQPnyKXOgCRJWhpJ/gR4LbC/qk5raccD7wdOAnYDr6+qr7fvLgcuBp4A3lxVH2vpZwLXACuAjwCXVlX1syyD4KTNN8743WWnH+SiQ3y/GHZf8Zq+Hk+Ses07q5IkLV/XAOumpG0GbqmqNcAt7TNJTgE2AKe2bd6Z5Ii2zdXAJmBNe03dpyRJc2awKknSMlVVfw38/ZTk9cC2trwNOK8r/fqqeryqHgR2AWclWQkcU1W3trup13ZtI0nSvNkNWJIkdRurqn0AVbUvyQktfRVwW9d6e1rad9ry1PSnSbKJzh1YxsbGmJiYmHcmDxw4wMTEBJedfnDe++insRX0Pa8LqV94so6HhfldfMOWZ/O7+BY7z4cNVh3PIkmSgEyTVodIf3pi1VZgK8DatWtrfHx83pmZmJhgfHy87+NA5+uy0w9y5c7+3iPYfcH4grafrONhYX4X37Dl2fwuvsXO82y6AV+D41kkSVouHmlde2nv+1v6HuDErvVWA3tb+upp0iVJWpDDBquOZ5EkaVnZAWxsyxuBG7rSNyQ5KsnJdC4839G6DD+W5OwkAS7s2kaSpHmbb3+URRvPAr0d0wJLM07kUJaiL/ow9oHvpeVefrAOwDpY7uXX0yV5HzAOvCDJHuCtwBXA9iQXAw8B5wNU1T1JtgP3AgeBS6rqibarN/HkUJ+b2kuSpAXp9eCJBY9ngd6OaQF4x3U39H2cyKEsdAzJfAxjH/heWu7lB+sArIPlXn49XVW9YYavzplh/S3AlmnS7wRO62HWJEma96NrHM8iSZIkSVo08w1WHc8iSZIkSVo0s3l0jeNZJEmSJEl9ddhg1fEskiRJkqR+m283YEmSJEmSFo3BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGzpFLnQFJkiT13kmbb1zQ9pedfpCLFriPSbuveE1P9iNpefHOqiRJkiRp4BisSpIkSZIGTt+D1STrktyfZFeSzf0+viRJWhy28ZKkXurrmNUkRwB/BLwK2AN8MsmOqrq3n/lYagsdQzIfM407cQyJJKkXbON1KP0495ntGFvPfaTh0e87q2cBu6rqC1X1beB6YH2f8yBJknrPNl6S1FP9ng14FfBw1+c9wI/0OQ/qshR3eWfilU5JGmq28RoKg3Lu08vZlvuln3n2vFDQ/2A106TV01ZKNgGb2scDSe5f4HFfAHx1gfsYam8egjrI7y3q7ge+/H1gHVgHw17+Fy91BnRIh23je9y+D9W/52Foh6catjyb38XXzzz36Lxw2Op42PILvcvztG18v4PVPcCJXZ9XA3unrlRVW4GtvTpokjuram2v9jeMlnsdLPfyg3UA1sFyL78W3WHb+F6278P273nY8gvDl2fzu/iGLc/md/Etdp77PWb1k8CaJCcneSawAdjR5zxIkqTes42XJPVUX++sVtXBJL8EfAw4AviTqrqnn3mQJEm9ZxsvSeq1fncDpqo+Anykz4ftWZfiIbbc62C5lx+sA7AOlnv5tcj63MYP27/nYcsvDF+eze/iG7Y8m9/Ft6h5TtXT5jeSJEmSJGlJ9XvMqiRJkiRJhzXywWqSdUnuT7Iryealzk+vJDkxyf9Icl+Se5Jc2tKPT3Jzkgfa+3Fd21ze6uH+JOd2pZ+ZZGf77qok0z1+YCAlOSLJp5N8uH1ebuU/NskHknyu/Vv40eVUB0l+tf37/2yS9yV51qiXP8mfJNmf5LNdaT0rc5Kjkry/pd+e5KS+FlA6jEFq1xf7/+Mi5Heozh3a3/Q7kvxdy+/vDnJ+u441VOcmSXa3Y92d5M5Bz3OG7NwnyUtb3U6+vpnkVwY8z7+aQTm/qqqRfdGZ4OHzwEuAZwJ/B5yy1PnqUdlWAi9vy88F/idwCvAfgc0tfTPwe235lFb+o4CTW70c0b67A/hROs/Iuwl49VKXbw718GvAe4EPt8/LrfzbgH/Tlp8JHLtc6gBYBTwIrGiftwMXjXr5gR8HXg58tiutZ2UG/i/gP7flDcD7l7rMvnxNvhiwdn2x/z8uQn6H6tyh7fs5bfkZwO3A2YOa3658D9W5CbAbeMGUtIHNM0N87kPnb9iX6TxTdCDzzICdX436ndWzgF1V9YWq+jZwPbB+ifPUE1W1r6o+1ZYfA+6j849rPZ3/xLT389ryeuD6qnq8qh4EdgFnJVkJHFNVt1bnX9W1XdsMtCSrgdcA7+pKXk7lP4bOidK7Aarq21X1KMuoDuhMErciyZHAs+k803Gky19Vfw38/ZTkXpa5e18fAM5ZzCv60hwNVLveh/+Pvc7vUJ07VMeB9vEZ7VWDml8YqXOTgczzCJz7nAN8vqq+OOB5Hpjzq1EPVlcBD3d93tPSRko63fR+mM4Vx7Gq2gedRgk4oa02U12sastT04fB24BfB/6pK205lf8lwFeAP23djd6V5GiWSR1U1ZeA3wceAvYB36iqj7NMyj9FL8v83W2q6iDwDeD5i5ZzaW6GoV0fir9Bw3Lu0LrU3g3sB26uqoHOL8N5blLAx5PclWTTgOd52M99NgDva8sDmedBO78a9WB1ursB1fdcLKIkzwH+HPiVqvrmoVadJq0OkT7QkrwW2F9Vd812k2nShrb8zZF0up9dXVU/DHyLTreMmYxUHbSxEuvpdDl5EXB0kjceapNp0oa2/LM0nzKPcn1o+A3zv8+B+Rs0TOcOVfVEVZ0BrKZzt+a0Q6y+pPkd4nOTV1TVy4FXA5ck+fFDrLvUeR7ac58kzwReB/zZ4VadJq2f/44H6vxq1IPVPcCJXZ9X07mNPRKSPINOY3NdVX2wJT/SbrvT3ve39JnqYk9bnpo+6F4BvC7JbjrdwH4qyXtYPuWHTt73tKvM0Omy+XKWTx28Eniwqr5SVd8BPgj8GMun/N16WebvbtO6/zyPp3dzlJbKMLTrA/03aFjPHVpXzwlg3QDndyjPTapqb3vfD3yITnf7Qc3zMJ/7vBr4VFU90j4Pap4H6vxq1IPVTwJrkpzcrmZsAHYscZ56oo0hezdwX1X9QddXO4CNbXkjcENX+oZ0Zvo8GVgD3NFu4z+W5Oy2zwu7thlYVXV5Va2uqpPo/K5/WVVvZJmUH6Cqvgw8nOSlLekc4F6WTx08BJyd5Nkt3+fQGX+1XMrfrZdl7t7Xv6Lzf2tY7lxp9A1Duz6wf4OG7dwhyQuTHNuWV9A5if7coOZ3GM9Nkhyd5LmTy8BPA58d1DwP+bnPG3iyC/Bk3gYxz4N1flWLPOvVUr+An6Ez293ngd9c6vz0sFz/gs6t9M8Ad7fXz9AZW3YL8EB7P75rm99s9XA/XbNxAWvp/GH6PPCHQJa6fHOsi3GenHFvWZUfOAO4s/07+G/AccupDoDfpXPi8lngv9KZiW6ky0+nodsHfIfOVcuLe1lm4Fl0uijtojOL30uWusy+fHW/BqldX+z/j4uQ36E6dwD+OfDplt/PAr/d0gcyv1PyPs4QnJvQGQP6d+11z+T/qQHP8xkM2bkPnUmKvgY8ryttYPPMAJ1fTZ6cSJIkSZI0MEa9G7AkSZIkaQgZrEqSJEmSBo7BqiRJkiRp4BisSpIkSZIGjsGqJEmSJGngGKxKkiRJkgaOwaokSZIkaeAYrEqSJEmSBs7/H2u5co50mCt2AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plotting histograms of multiple columns together\n", "RtData.hist(['Longitude', 'Latitude', \n", " 'Votes', 'Average Cost for two'], figsize=(16,8))" ] }, { "cell_type": "markdown", "id": "5dfd795e", "metadata": {}, "source": [ "## Histogram Interpretation:\n", "\n", "The ideal outcome for histogram is a bell curve or slightly skewed bell curve. If there is too much skewness, then outlier treatment should be done and the column should be re-examined, if that also does not solve the problem then only reject the column.\n", "\n", "Selected Continuous Variables:\n", "\n", "* Longitude : Selected. The distribution is good.\n", "* Latitude: Selected. The distribution is good.\n", "* Average Cost for two: Selected. Outliers seen beyond 300000, need to treat them.\n", "* Votes: Selected. Outliers seen beyond 4000, need to treat them." ] }, { "cell_type": "markdown", "id": "facf6910", "metadata": {}, "source": [ "## Outliers\n", "\n", "Outliers bias the training of machine learning models. As the algorithm tries to fit the extreme value, it goes away from majority of the data.\n", "\n", "There are below two options to treat outliers in the data.\n", "\n", "* Option-1: Delete the outlier Records. Only if there are just few rows lost.\n", "* Option-2: Impute the outlier values with a logical business value\n", "\n", "Below we are finding out the most logical value to be replaced in place of outliers by looking at the histogram." ] }, { "cell_type": "markdown", "id": "dbfdd016", "metadata": {}, "source": [ "### Replacing outliers for 'Votes'" ] }, { "cell_type": "code", "execution_count": 17, "id": "feb3fec7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7863 3986\n", "821 3848\n", "3992 3697\n", "3119 3591\n", "1861 3569\n", " ... \n", "6102 0\n", "6099 0\n", "6098 0\n", "6091 0\n", "5427 0\n", "Name: Votes, Length: 8624, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Finding nearest values to 4000 mark \n", "RtData['Votes'][RtData['Votes']<4000].sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 18, "id": "61bf5b31", "metadata": {}, "outputs": [], "source": [ "#Above result shows the nearest logical value is 3986, hence, replacing any value above 4000 with it.\n", "# Replacing outliers with nearest possibe value\n", "RtData['Votes'][RtData['Votes']>4000] =3986\n" ] }, { "cell_type": "markdown", "id": "299fddb7", "metadata": {}, "source": [ "### Replacing outliers for 'Average Cost for two'" ] }, { "cell_type": "code", "execution_count": 19, "id": "777746a0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7543 8000\n", "4088 7000\n", "4087 6500\n", "7499 6000\n", "7538 6000\n", " ... \n", "5577 50\n", "7775 50\n", "7808 50\n", "7830 50\n", "7418 50\n", "Name: Average Cost for two, Length: 8643, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Finding nearest values to 50000 mark\n", "RtData['Average Cost for two'][RtData['Average Cost for two']<50000].sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 20, "id": "166677a9", "metadata": {}, "outputs": [], "source": [ "#Above result shows the nearest logical value is 8000, hence, replacing any value above 50000 with it.\n", "## Replacing outliers with nearest possibe value\n", "RtData['Average Cost for two'][RtData['Average Cost for two']>50000] = 8000" ] }, { "cell_type": "markdown", "id": "88a14236", "metadata": {}, "source": [ "## Visualizing distribution after outlier treatment" ] }, { "cell_type": "markdown", "id": "be501277", "metadata": {}, "source": [ "The distribution has improved after the outlier treatment. There is still a tail but it is thick, that means there are many values in that range, hence, it is acceptable." ] }, { "cell_type": "code", "execution_count": 21, "id": "b95a1acc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ]],\n", " dtype=object)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MxM9Icnd774okOVSJSZIkSZKkpbFgsaGq7q+q06vqdOAM4O+BG4BLgFurag1wa3tNkpOBDcApwDrgqiSHtc1dDWwC1rTHuk6zkSRJkiRJS+5Ah1GcDXyhqr4ErAe2tvhW4Ny2vB64vqoer6oHge3AmUlWAkdV1W1VVcC1A+tIkiRJkqQxcfgBtt8AfKAtT1XVboCq2p3kuBZfBXxiYJ2dLfbttjw7/jRJNtHvAcHU1BS9Xu8AD3N+Uyvg4tP2dba9Lg2b5969ezv9mSwV8xgt5jE6xiEHMA9JkqRJNnSxIclzgDcAly7UdI5Y7Sf+9GDVFmALwNq1a2t6enrYw1zQldfdyOV3H2iNZXHsOH96qHa9Xo8ufyZLxTxGi3mMjnHIAcxDkiRpkh3IMIrXAJ+qqkfa60fa0Aja854W3wkcP7DeamBXi6+eIy5JkiRJksbIgRQb3siTQygAbgI2tuWNwI0D8Q1JjkhyIv2JIO9oQy4eS3JWuwvFBQPrSJIkLaokRyf5YJLPJ7kvyQ96ty1JkroxVLEhyfOAnwA+NBC+DPiJJA+09y4DqKp7gG3AvcBHgYuq6om2zluB99KfNPILwM0d5CBJkvRMvAf4aFV9H/AK4D6825YkSZ0YavKCqvp74EWzYo/SvzvFXO03A5vniN8JnHrghylJktSdJEcBPwy8BaCqvgV8K8l6YLo12wr0gF9l4G5bwINJZu62tYN2t6223Zm7bfmFiiRpoh3orS8lSZLGwcuBrwJ/mOTTSd6b5Ehm3W0LGLzb1sMD68/cVWsVQ95tS5KkSTKat2WQJEk6tA4HXgW8rapuT/Ie2pCJeRzU3bYOxW29Z27LOqq39J6xUK7jcntZ8xgt45DHOOQA5jFqFjMPiw2SJGkS7QR2VtXt7fUH6RcbHkmysqp2d3m3rUNxW++Z27K+5ZKPHPS2DqWFbu09LreXNY/RMg55jEMOYB6jZjHzcBiFJEmaOFX1FeDhJCe10Nn0J7f2bluSJHXAng2SJGlSvQ24LslzgC8CP0v/i5htSS4EHgLOg/7dtpLM3G1rH0+/29Y1wAr6E0M6OaQkaeJZbJAkSROpqu4C1s7xlnfbkiTpIDmMQpIkSZIkdcpigyRJkiRJ6pTFBkmSJEmS1CmLDZIkSZIkqVMWGyRJkiRJUqcsNkiSJEmSpE5ZbJAkSZIkSZ2y2CBJkiRJkjplsUGSJEmSJHXKYoMkSZIkSeqUxQZJkiRJktQpiw2SJEmSJKlTFhskSZIkSVKnLDZIkiRJkqROWWyQJEmSJEmdstggSZIkSZI6ZbFBkiRJkiR1aqhiQ5Kjk3wwyeeT3JfkB5Mcm+SWJA+052MG2l+aZHuS+5OcMxA/I8nd7b0rkuRQJCVJkiRJkpbOsD0b3gN8tKq+D3gFcB9wCXBrVa0Bbm2vSXIysAE4BVgHXJXksLadq4FNwJr2WNdRHpIkSZIkaUQsWGxIchTww8DvA1TVt6rq68B6YGtrthU4ty2vB66vqser6kFgO3BmkpXAUVV1W1UVcO3AOpIkSZIkaUwM07Ph5cBXgT9M8ukk701yJDBVVbsB2vNxrf0q4OGB9Xe22Kq2PDsuSZIkSZLGyOFDtnkV8Laquj3Je2hDJuYx1zwMtZ/40zeQbKI/3IKpqSl6vd4QhzmcqRVw8Wn7Ottel4bNc+/evZ3+TJaKeYwW8xgd45ADmIckSdIkG6bYsBPYWVW3t9cfpF9seCTJyqra3YZI7Blof/zA+quBXS2+eo7401TVFmALwNq1a2t6enq4bIZw5XU3cvndw6S9+HacPz1Uu16vR5c/k6ViHqPFPEbHOOQA5iFJkjTJFhxGUVVfAR5OclILnQ3cC9wEbGyxjcCNbfkmYEOSI5KcSH8iyDvaUIvHkpzV7kJxwcA6kiRJkiRpTAz7Ff/bgOuSPAf4IvCz9AsV25JcCDwEnAdQVfck2Ua/ILEPuKiqnmjbeStwDbACuLk9JEmSJEnSGBmq2FBVdwFr53jr7HnabwY2zxG/Ezj1AI5PkiRJkiQtM8PcjUKSJEmSJGloFhskSZIkSVKnLDZIkqSJlGRHkruT3JXkzhY7NsktSR5oz8cMtL80yfYk9yc5ZyB+RtvO9iRXtImwJUmaaBYbJEnSJPvRqjq9qmbmproEuLWq1gC3ttckORnYAJwCrAOuSnJYW+dqYBP9O3Ctae9LkjTRLDZIkiQ9aT2wtS1vBc4diF9fVY9X1YPAduDMJCuBo6rqtqoq4NqBdSRJmlgWGyRJ0qQq4GNJPplkU4tNVdVugPZ8XIuvAh4eWHdni61qy7PjkiRNtKFufSlJkjSGXl1Vu5IcB9yS5PP7aTvXPAy1n/hTV+4XMzYBTE1N0ev1nsHhPtXevXvp9XpcfNq+g97WobRQrjN5LHfmMVrGIY9xyAHMY9QsZh4WGyRJ0kSqql3teU+SG4AzgUeSrKyq3W2IxJ7WfCdw/MDqq4FdLb56jvjsfW0BtgCsXbu2pqenD/r4e70e09PTvOWSjxz0tg6lHedP7/f9mTyWO/MYLeOQxzjkAOYxahYzD4dRSJKkiZPkyCQvmFkGfhL4HHATsLE12wjc2JZvAjYkOSLJifQngryjDbV4LMlZ7S4UFwysI0nSxLJngyRJmkRTwA3tLpWHA++vqo8m+WtgW5ILgYeA8wCq6p4k24B7gX3ARVX1RNvWW4FrgBXAze0hSdJEs9ggSZImTlV9EXjFHPFHgbPnWWczsHmO+J3AqV0foyRJy5nDKCRJkiRJUqcsNkiSJEmSpE5ZbJAkSZIkSZ2y2CBJkiRJkjplsUGSJEmSJHXKYoMkSZIkSeqUxQZJkiRJktQpiw2SJEmSJKlTFhskSZIkSVKnLDZIkiRJkqROWWyQJEmSJEmdstggSZIkSZI6NVSxIcmOJHcnuSvJnS12bJJbkjzQno8ZaH9pku1J7k9yzkD8jLad7UmuSJLuU5IkSZIkSUvpQHo2/GhVnV5Va9vrS4Bbq2oNcGt7TZKTgQ3AKcA64Kokh7V1rgY2AWvaY93BpyBJkiRJkkbJwQyjWA9sbctbgXMH4tdX1eNV9SCwHTgzyUrgqKq6raoKuHZgHUmSJEmSNCaGLTYU8LEkn0yyqcWmqmo3QHs+rsVXAQ8PrLuzxVa15dlxSZIkSZI0Rg4fst2rq2pXkuOAW5J8fj9t55qHofYTf/oG+gWNTQBTU1P0er0hD3NhUyvg4tP2dba9Lg2b5969ezv9mSwV8xgt5jE6xiEHMA9JkqRJNlSxoap2tec9SW4AzgQeSbKyqna3IRJ7WvOdwPEDq68GdrX46jnic+1vC7AFYO3atTU9PT10Qgu58robufzuYWssi2vH+dNDtev1enT5M1kq5jFazGN0jEMOYB6SJEmTbMFhFEmOTPKCmWXgJ4HPATcBG1uzjcCNbfkmYEOSI5KcSH8iyDvaUIvHkpzV7kJxwcA6kiRJkiRpTAzzFf8UcEO7S+XhwPur6qNJ/hrYluRC4CHgPICquifJNuBeYB9wUVU90bb1VuAaYAVwc3tIkiRJkqQxsmCxoaq+CLxijvijwNnzrLMZ2DxH/E7g1AM/TEmSJEmStFwczK0vJUmSJEmSnsZigyRJkiRJ6pTFBkmSJEmS1CmLDZIkSZIkqVMWGyRJkiRJUqcsNkiSJEmSpE5ZbJAkSRMpyWFJPp3kw+31sUluSfJAez5moO2lSbYnuT/JOQPxM5Lc3d67IkmWIhdJkkaNxQZJkjSp3g7cN/D6EuDWqloD3Npek+RkYANwCrAOuCrJYW2dq4FNwJr2WLc4hy5J0miz2CBJkiZOktXA64D3DoTXA1vb8lbg3IH49VX1eFU9CGwHzkyyEjiqqm6rqgKuHVhHkqSJZrFBkiRNoncDvwJ8ZyA2VVW7AdrzcS2+Cnh4oN3OFlvVlmfHJUmaeIcv9QFIkiQtpiSvB/ZU1SeTTA+zyhyx2k98rn1uoj/cgqmpKXq93lDHuj979+6l1+tx8Wn7Dnpbh9JCuc7ksdyZx2gZhzzGIQcwj1GzmHlYbJAkSZPm1cAbkrwWeC5wVJL3AY8kWVlVu9sQiT2t/U7g+IH1VwO7Wnz1HPGnqaotwBaAtWvX1vT09EEn0ev1mJ6e5i2XfOSgt3Uo7Th/er/vz+Sx3JnHaBmHPMYhBzCPUbOYeTiMQpIkTZSqurSqVlfVCfQnfvx4Vb0JuAnY2JptBG5syzcBG5IckeRE+hNB3tGGWjyW5Kx2F4oLBtaRJGmi2bNBkiSp7zJgW5ILgYeA8wCq6p4k24B7gX3ARVX1RFvnrcA1wArg5vaQJGniWWyQJEkTq6p6QK8tPwqcPU+7zcDmOeJ3AqceuiOUJGl5chiFJEmSJEnqlMUGSZIkSZLUKYsNkiRJkiSpUxYbJEmSJElSpyw2SJIkSZKkTllskCRJkiRJnbLYIEmSJEmSOmWxQZIkSZIkdcpigyRJkiRJ6tTQxYYkhyX5dJIPt9fHJrklyQPt+ZiBtpcm2Z7k/iTnDMTPSHJ3e++KJOk2HUmSJEmStNQOpGfD24H7Bl5fAtxaVWuAW9trkpwMbABOAdYBVyU5rK1zNbAJWNMe6w7q6CVJkiRJ0sgZqtiQZDXwOuC9A+H1wNa2vBU4dyB+fVU9XlUPAtuBM5OsBI6qqtuqqoBrB9aRJEmSJElj4vAh270b+BXgBQOxqaraDVBVu5Mc1+KrgE8MtNvZYt9uy7PjT5NkE/0eEExNTdHr9YY8zIVNrYCLT9vX2fa6NGyee/fu7fRnslTMY7SYx+gYhxzAPCRJkibZgsWGJK8H9lTVJ5NMD7HNueZhqP3Enx6s2gJsAVi7dm1NTw+z2+Fced2NXH73sDWWxbXj/Omh2vV6Pbr8mSwV8xgt5jE6xiEHMA9JkqRJNsxf3a8G3pDktcBzgaOSvA94JMnK1qthJbCntd8JHD+w/mpgV4uvniMuSZIkSZLGyIJzNlTVpVW1uqpOoD/x48er6k3ATcDG1mwjcGNbvgnYkOSIJCfSnwjyjjbk4rEkZ7W7UFwwsI4kSZIkSRoTBzOe4DJgW5ILgYeA8wCq6p4k24B7gX3ARVX1RFvnrcA1wArg5vaQJEmSJElj5ICKDVXVA3pt+VHg7HnabQY2zxG/Ezj1QA9SkiRJkiQtH0Pd+lKSJEmSJGlYFhskSZIkSVKnLDZIkiRJkqROWWyQJEmSJEmdstggSZIkSZI6ZbFBkiRJkiR1ymKDJEmSJEnqlMUGSZIkSZLUKYsNkiRJkiSpUxYbJEmSJElSpyw2SJKkiZPkuUnuSPKZJPckeVeLH5vkliQPtOdjBta5NMn2JPcnOWcgfkaSu9t7VyTJUuQkSdIosdggSZIm0ePAj1XVK4DTgXVJzgIuAW6tqjXAre01SU4GNgCnAOuAq5Ic1rZ1NbAJWNMe6xYxD0mSRpLFBkmSNHGqb297+ez2KGA9sLXFtwLntuX1wPVV9XhVPQhsB85MshI4qqpuq6oCrh1YR5KkiWWxQZIkTaQkhyW5C9gD3FJVtwNTVbUboD0f15qvAh4eWH1ni61qy7PjkiRNtMOX+gAkSZKWQlU9AZye5GjghiSn7qf5XPMw1H7iT1052UR/qAVTU1P0er0DPt7Z9u7dS6/X4+LT9h30tg6lhXKdyWO5M4/RMg55jEMOYB6jZjHzsNggSZImWlV9PUmP/lwLjyRZWVW72xCJPa3ZTuD4gdVWA7tafPUc8dn72AJsAVi7dm1NT08f9HH3ej2mp6d5yyUfOehtHUo7zp/e7/szeSx35jFaxiGPccgBzGPULGYeDqOQJEkTJ8lLWo8GkqwAfhz4PHATsLE12wjc2JZvAjYkOSLJifQngryjDbV4LMlZ7S4UFwysI0nSxLJngyRJmkQrga3tjhLPArZV1YeT3AZsS3Ih8BBwHkBV3ZNkG3AvsA+4qA3DAHgrcA2wAri5PSRJmmgWGyRJ0sSpqs8Cr5wj/ihw9jzrbAY2zxG/E9jffA+SJE0ch1FIkiRJkqROWWyQJEmSJEmdstggSZIkSZI6ZbFBkiRJkiR1asFiQ5LnJrkjyWeS3JPkXS1+bJJbkjzQno8ZWOfSJNuT3J/knIH4GUnubu9d0W4RJUmSJEmSxsgwPRseB36sql4BnA6sS3IWcAlwa1WtAW5tr0lyMrABOAVYB1zVbisFcDWwif69qde09yVJkiRJ0hhZsNhQfXvby2e3RwHrga0tvhU4ty2vB66vqser6kFgO3BmkpXAUVV1W1UVcO3AOpIkSZIkaUwMNWdDksOS3AXsAW6pqtuBqaraDdCej2vNVwEPD6y+s8VWteXZcUmSJEmSNEYOH6ZRVT0BnJ7kaOCGJKfup/lc8zDUfuJP30Cyif5wC6ampuj1esMc5lCmVsDFp+3rbHtdGjbPvXv3dvozWSrmMVrMY3SMQw5gHpIkSZNsqGLDjKr6epIe/bkWHkmysqp2tyESe1qzncDxA6utBna1+Oo54nPtZwuwBWDt2rU1PT19IIe5X1dedyOX331AaS+aHedPD9Wu1+vR5c9kqZjHaDGP0TEOOYB5SJIkTbJh7kbxktajgSQrgB8HPg/cBGxszTYCN7blm4ANSY5IciL9iSDvaEMtHktyVrsLxQUD60iSJEmSpDExzFf8K4Gt7Y4SzwK2VdWHk9wGbEtyIfAQcB5AVd2TZBtwL7APuKgNwwB4K3ANsAK4uT0kSZIkSdIYWbDYUFWfBV45R/xR4Ox51tkMbJ4jfiewv/keJEmSJEnSMjfU3SgkSZIkSZKGZbFBkiRJkiR1ymKDJEmSJEnqlMUGSZIkSZLUKYsNkiRJkiSpUxYbJEmSJElSpyw2SJIkSZKkTllskCRJkiRJnbLYIEmSJEmSOmWxQZIkSZIkdcpigyRJkiRJ6pTFBkmSJEmS1CmLDZIkSZIkqVMWGyRJkiRJUqcsNkiSJEmSpE5ZbJAkSRMnyfFJ/nuS+5Lck+TtLX5skluSPNCejxlY59Ik25Pcn+ScgfgZSe5u712RJEuRkyRJo8RigyRJmkT7gIur6vuBs4CLkpwMXALcWlVrgFvba9p7G4BTgHXAVUkOa9u6GtgErGmPdYuZiCRJo8higyRJmjhVtbuqPtWWHwPuA1YB64GtrdlW4Ny2vB64vqoer6oHge3AmUlWAkdV1W1VVcC1A+tIkjSxDl/qA5AkSVpKSU4AXgncDkxV1W7oFySSHNearQI+MbDazhb7dlueHZ+9j030ez8wNTVFr9c76OPeu3cvvV6Pi0/bd9DbOpQWynUmj+XOPEbLOOQxDjmAeYyaxczDYoMkSZpYSZ4P/Anwjqr6xn6mW5jrjdpP/KmBqi3AFoC1a9fW9PT0MzreQb1ej+npad5yyUcOeluH0o7zp/f7/kwey515jJZxyGMccgDzGDWLmYfDKCRJ0kRK8mz6hYbrqupDLfxIGxpBe97T4juB4wdWXw3savHVc8QlSZpoFhskSdLEaXeM+H3gvqr67YG3bgI2tuWNwI0D8Q1JjkhyIv2JIO9oQy4eS3JW2+YFA+tIkjSxHEYhSZIm0auBNwN3J7mrxX4NuAzYluRC4CHgPICquifJNuBe+neyuKiqnmjrvRW4BlgB3NwekiRNtAWLDUmOpz+z8ncB3wG2VNV7khwL/BFwArAD+Kmq+tu2zqXAhcATwC9W1Z+1+Bk8+WH8p8Db28zNkiRJi6aq/pK551sAOHuedTYDm+eI3wmc2t3RSZK0/A0zjML7UEuSJEmSpKEtWGzwPtSSJEmSJOlAHNAEkfu7DzUweB/qhwdWm7nf9CqGuA+1JEmSJEla3oaeIHKx7kPd9rWJ/nALpqam6PV6wx7mgqZWwMWn7etse10aNs+9e/d2+jNZKuYxWsxjdIxDDmAekiRJk2yoYsP+7kNdVbu7vg91VW0BtgCsXbu2pqenh8tmCFdedyOX3z2aN+HYcf70UO16vR5d/kyWinmMFvMYHeOQA5iHJEnSJFtwGIX3oZYkSZIkSQdimK/4vQ+1JEmSJEka2oLFBu9DLUmSJEmSDsQB3Y1CkiRJkiRpIRYbJEmSJElSpyw2SJIkSZKkTllskCRJkiRJnbLYIEmSJEmSOmWxQZIkSZIkdcpigyRJkiRJ6pTFBkmSJEmS1CmLDZIkSZIkqVMWGyRJkiRJUqcsNkiSJEmSpE5ZbJAkSZIkSZ2y2CBJkiRJkjplsUGSJEmSJHXKYoMkSZIkSeqUxQZJkiRJktQpiw2SJEmSJKlTFhskSZIkSVKnLDZIkiRJkqROHb7UByBJkrTYkvwB8HpgT1Wd2mLHAn8EnADsAH6qqv62vXcpcCHwBPCLVfVnLX4GcA2wAvhT4O1VVYuZy6g74ZKP7Pf9i0/bx1sWaHMo7bjsdUu2b0kaZ/ZskCRJk+gaYN2s2CXArVW1Bri1vSbJycAG4JS2zlVJDmvrXA1sAta0x+xtSpI0kSw2SJKkiVNVfwH8zazwemBrW94KnDsQv76qHq+qB4HtwJlJVgJHVdVtrTfDtQPrSJI00Sw2SJIk9U1V1W6A9nxci68CHh5ot7PFVrXl2XFJkiaeczZIkiTtX+aI1X7iT99Ason+cAumpqbo9XoHfVB79+6l1+tx8Wn7DnpbS2lqBUuaQxfnAp48H8udeYyOccgBzGPULGYeCxYbnEBJkiRNiEeSrKyq3W2IxJ4W3wkcP9BuNbCrxVfPEX+aqtoCbAFYu3ZtTU9PH/TB9no9pqenl3RyxS5cfNo+Lr976b7/2nH+dCfbmTkfy515jI5xyAHMY9QsZh7DDKO4BidQkiRJ4+8mYGNb3gjcOBDfkOSIJCfSv465ow21eCzJWUkCXDCwjiRJE23BYoMTKEmSpHGT5APAbcBJSXYmuRC4DPiJJA8AP9FeU1X3ANuAe4GPAhdV1RNtU28F3kv/mucLwM2LmogkSSPqmfZZe8oESkkGJ1D6xEC7mYmSvs0BTKB0KMY1/uOBL/G4wP0ZNk/HC40W8xgt45DHOOQA5qHRVlVvnOets+dpvxnYPEf8TuDUDg9NkqSx0PUAuYOeQAkOzbjGGVded+OSjgvcn2HHDDpeaLSYx2gZhzzGIQcwD0mSpEn2TG99+UgbGkHXEyhJkiRJkqTl7ZkWG5xASZIkSZIkzWmYW19+AJgGXpxkJ/Cb9CdM2tYmU3oIOA/6EyglmZlAaR9Pn0DpGvq3vrwZJ1CSJEmSJGksLVhscAIlSZIkSZJ0IJ7pMApJkiRJkqQ5WWyQJEmSJEmdstggSZIkSZI6ZbFBkiRJkiR1ymKDJEmSJEnqlMUGSZIkSZLUKYsNkiRJkiSpUxYbJEmSJElSpyw2SJIkSZKkTllskCRJkiRJnbLYIEmSJEmSOmWxQZIkSZIkdcpigyRJkiRJ6pTFBkmSJEmS1CmLDZIkSZIkqVMWGyRJkiRJUqcOX+oD0JNOuOQjQ7W7+LR9vGXItl3acdnrFn2fkiRJh9Kw118LORTXZ157SVrO7NkgSZIkSZI6ZbFBkiRJkiR1ymKDJEmSJEnqlMUGSZIkSZLUKYsNkiRJkiSpUxYbJEmSJElSpxb91pdJ1gHvAQ4D3ltVly32MeiZ6erWUDO6vkWUt4eSJC0Vr290KHR97TWMA7k+89pL0v4sas+GJIcB/xF4DXAy8MYkJy/mMUiSJHXJ6xtJkp5usXs2nAlsr6ovAiS5HlgP3LvIx6ExtBTVf+i+h8ZS8JsJSTooXt9IkjTLYhcbVgEPD7zeCfzAIh+DpFlOuOQjY1E0gcUv/ox6oWapinBgIU4TxesbTaSl/IxZyDh8Bi1lDn7+qQupqsXbWXIecE5V/Vx7/WbgzKp626x2m4BN7eVJwP0dHsaLga91uL2lMA45gHmMGvMYHeOQA5jHbC+rqpd0sB2NmGGubw7RtY2/Y6PFPEbLOOQxDjmAeYyarvOY9/pmsXs27ASOH3i9Gtg1u1FVbQG2HIoDSHJnVa09FNteLOOQA5jHqDGP0TEOOYB5aKIseH1zKK5txuXfpnmMFvMYHeOQA5jHqFnMPBb71pd/DaxJcmKS5wAbgJsW+RgkSZK65PWNJEmzLGrPhqral+QXgD+jf2uoP6iqexbzGCRJkrrk9Y0kSU+32MMoqKo/Bf50sfc74JAMz1hk45ADmMeoMY/RMQ45gHlogizR9c24/Ns0j9FiHqNjHHIA8xg1i5bHok4QKUmSJEmSxt9iz9kgSZIkSZLG3MQUG5KsS3J/ku1JLlnq41lIkh1J7k5yV5I7W+zYJLckeaA9HzPQ/tKW2/1JzlnC4/6DJHuSfG4gdsDHneSMlv/2JFckyRLn8M4kX27n464krx3lHNr+j0/y35Pcl+SeJG9v8eV2PubLY9mckyTPTXJHks+0HN7V4svtXMyXx7I5F7PyOSzJp5N8uL1eVudDkysjfk0zz+fosvv9GqPP0bH4DGr7H4v/t9PRdf4S/7s6OskHk3y+/Y784DLM4aQ8ee1yV5JvJHnHcsuj7f+X2u/355J8oP3eL30eVTX2D/qTNX0BeDnwHOAzwMlLfVwLHPMO4MWzYv8euKQtXwL8u7Z8csvpCODEluthS3TcPwy8CvjcwRw3cAfwg0CAm4HXLHEO7wT+nznajmQObf8rgVe15RcA/6sd73I7H/PlsWzOSdvf89vys4HbgbOW4bmYL49lcy5mHd8vA+8HPtxeL6vz4WMyHyyDaxrG4Fqg7X9cPkfH4jOo7X8s/t+mo+v8Jf53tRX4ubb8HODo5ZbDrHwOA74CvGy55QGsAh4EVrTX24C3jEIek9Kz4Uxge1V9saq+BVwPrF/iY3om1tP/xaY9nzsQv76qHq+qB4Ht9HNedFX1F8DfzAof0HEnWQkcVVW3Vf9f/bUD6xxy8+Qwn5HMAaCqdlfVp9ryY8B99P8zWm7nY7485jNyeVTf3vby2e1RLL9zMV8e8xnJPACSrAZeB7x31vEum/OhiTXy1zTjcC0AY/U5OhafQRPw//ayySXJUfSLir8PUFXfqqqvL6cc5nA28IWq+hLLM4/DgRVJDgeeB+xiBPKYlGLDKuDhgdc72f8fK6OggI8l+WSSTS02VVW7of8BCBzX4qOe34Ee96q2PDu+1H4hyWfT7x460w1pWeSQ5ATglfS/zVi252NWHrCMzknr+nkXsAe4paqW5bmYJw9YRueieTfwK8B3BmLL7nxoIo36Z/58lvXv13L/HB2Tz6B3Mz7/b3dxnb+Uubwc+Crwh+kPa3lvkiNZXjnMtgH4QFteVnlU1ZeB3wIeAnYDf1dVH2ME8piUYsNcY032923cKHh1Vb0KeA1wUZIf3k/b5ZgfzH/co5jP1cB3A6fT/yW+vMVHPockzwf+BHhHVX1jf03niI1MLnPksazOSVU9UVWnA6vpV49P3U/zkcwB5s1jWZ2LJK8H9lTVJ4ddZY7YkuehiTVu/+5G/vdrHD5Hl/tn0Bj+v93Fdf5S5nI4/aFSV1fVK4Fv0u+mP59RzOEfJXkO8AbgjxdqOkdsyfNoX/Kspz8k4qXAkUnetL9V5ogdkjwmpdiwEzh+4PVq+l1LRlZV7WrPe4Ab6HebfKR1b6E972nNRz2/Az3unW15dnzJVNUj7YP6O8Dv8eQwlZHOIcmz6V8gXVdVH2rhZXc+5spjuZ6T1s2wB6xjGZ6LGYN5LMNz8WrgDUl20O+C/mNJ3scyPh+aKKP+mT+fZfn7NS6fozOW8WfQWP2/3dF1/lLmshPYOdC78YP0iw/LKYdBrwE+VVWPtNfLLY8fBx6sqq9W1beBDwE/xAjkMSnFhr8G1iQ5sVWuNgA3LfExzSvJkUleMLMM/CTwOfrHvLE12wjc2JZvAjYkOSLJicAa+pN7jIoDOu7WzeexJGe1GVAvGFhnScz8ojb/gv75gBHOoe3394H7quq3B95aVudjvjyW0zlJ8pIkR7flFfQ/FD7P8jsXc+axnM4FQFVdWlWrq+oE+p8HH6+qN7HMzocm1rK6phmw7H6/xuhzdNl/Bo3T/9tdXecv8fn4CvBwkpNa6Gzg3uWUwyxv5MkhFLD88ngIOCvJ89r+z6Y/x8zS51FLMNvnUjyA19KfRfgLwK8v9fEscKwvpz9D6GeAe2aOF3gRcCvwQHs+dmCdX2+53c8SzoZO/xd1N/Bt+tWxC5/JcQNr6f/H+wXgd4AscQ7/Gbgb+Gz7BV05yjm0/f8z+l2fPgvc1R6vXYbnY748ls05Af4J8Ol2rJ8D/nWLL7dzMV8ey+ZczJHTNE/Oar6szoePyX0w4tc0jMG1QNv/uHyOjsVn0MAxLOv/t+nwOn+J/12dDtzZ/l39F+CY5ZZD2//zgEeBFw7ElmMe76JfRPwc/euyI0Yhj7SNSpIkSZIkdWJShlFIkiRJkqRFYrFBkiRJkiR1ymKDJEmSJEnqlMUGSZIkSZLUKYsNkiRJkiSpUxYbJEmSJElSpyw2SJIkSZKkTllskCRJkiRJnfr/AbaTemvHRxcnAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "RtData.hist(['Votes', 'Average Cost for two'], figsize=(18,5))" ] }, { "cell_type": "markdown", "id": "7c84adf2", "metadata": {}, "source": [ "## Missing values treatment" ] }, { "cell_type": "code", "execution_count": 22, "id": "1a65122a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Longitude 0\n", "Latitude 0\n", "Has Table booking 0\n", "Has Online delivery 0\n", "Is delivering now 0\n", "Price range 0\n", "Votes 0\n", "Average Cost for two 0\n", "Rating 0\n", "dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Finding how many missing values are there for each column\n", "RtData.isnull().sum()" ] }, { "cell_type": "markdown", "id": "1898443a", "metadata": {}, "source": [ "No missing values in this data." ] }, { "cell_type": "markdown", "id": "1d68f401", "metadata": {}, "source": [ "# Feature Selection\n", "Now its time to finally choose the best columns(Features) which are correlated to the Target variable. This can be done directly by measuring the correlation values or ANOVA/Chi-Square tests. However, it is always helpful to visualize the relation between the Target variable and each of the predictors to get a better sense of data." ] }, { "cell_type": "markdown", "id": "9adf0b1d", "metadata": {}, "source": [ "In this case study the Target variable is Continuous, hence below two scenarios will be present\n", "\n", "* Continuous Target Variable Vs Continuous Predictor\n", "* Continuous Target Variable Vs Categorical Predictor" ] }, { "cell_type": "markdown", "id": "9922c5dc", "metadata": {}, "source": [ "## Relationship exploration: Continuous Vs Continuous -- Scatter Charts" ] }, { "cell_type": "code", "execution_count": 23, "id": "8cacda0b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ContinuousCols=['Longitude', 'Latitude', 'Votes', 'Average Cost for two']\n", "\n", "# Plotting scatter chart for each predictor vs the target variable\n", "for predictor in ContinuousCols:\n", " RtData.plot.scatter(x=predictor, y='Rating', figsize=(10,5), title=predictor+\" VS \"+ 'Rating')" ] }, { "cell_type": "markdown", "id": "78c2e273", "metadata": {}, "source": [ "# Statistical Feature Selection (Continuous Vs Continuous) using Correlation value" ] }, { "cell_type": "code", "execution_count": 24, "id": "2d3c371a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RatingLongitudeLatitudeVotesAverage Cost for two
Rating1.0000000.1618000.0651560.3581310.366275
Longitude0.1618001.0000000.9265900.0764110.059605
Latitude0.0651560.9265901.000000-0.0351780.017787
Votes0.3581310.076411-0.0351781.0000000.314376
Average Cost for two0.3662750.0596050.0177870.3143761.000000
\n", "
" ], "text/plain": [ " Rating Longitude Latitude Votes \\\n", "Rating 1.000000 0.161800 0.065156 0.358131 \n", "Longitude 0.161800 1.000000 0.926590 0.076411 \n", "Latitude 0.065156 0.926590 1.000000 -0.035178 \n", "Votes 0.358131 0.076411 -0.035178 1.000000 \n", "Average Cost for two 0.366275 0.059605 0.017787 0.314376 \n", "\n", " Average Cost for two \n", "Rating 0.366275 \n", "Longitude 0.059605 \n", "Latitude 0.017787 \n", "Votes 0.314376 \n", "Average Cost for two 1.000000 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating correlation matrix\n", "ContinuousCols=['Rating','Longitude', 'Latitude', 'Votes', 'Average Cost for two']\n", "\n", "# Creating the correlation matrix\n", "CorrelationData=RtData[ContinuousCols].corr()\n", "CorrelationData" ] }, { "cell_type": "code", "execution_count": 25, "id": "0fd7f7a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rating 1.000000\n", "Votes 0.358131\n", "Average Cost for two 0.366275\n", "Name: Rating, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filtering only those columns where absolute correlation > 0.5 with Target Variable\n", "# reducing the 0.5 threshold if no variable is selected like in this case\n", "CorrelationData['Rating'][abs(CorrelationData['Rating']) > 0.3 ]\n" ] }, { "cell_type": "markdown", "id": "4c343a14", "metadata": {}, "source": [ "Final selected Continuous columns:\n", "\n", "'Votes', 'Average Cost for two'" ] }, { "cell_type": "markdown", "id": "67ab87df", "metadata": {}, "source": [ "# Relationship exploration: Categorical Vs Continuous -- Box Plots\n" ] }, { "cell_type": "markdown", "id": "d5a1137f", "metadata": {}, "source": [ "When the target variable is Continuous and the predictor variable is Categorical we analyze the relation using Boxplots and measure the strength of relation using Anova test" ] }, { "cell_type": "code", "execution_count": 26, "id": "02cab522", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Box plots for Categorical Target Variable \"Rating\" and continuous predictors\n", "CategoricalColsList=['Has Table booking', 'Has Online delivery', 'Price range']\n", "\n", "import matplotlib.pyplot as plt\n", "fig, PlotCanvas=plt.subplots(nrows=1, ncols=len(CategoricalColsList), figsize=(18,5))\n", "\n", "# Creating box plots for each continuous predictor against the Target Variable \"Rating\"\n", "for PredictorCol , i in zip(CategoricalColsList, range(len(CategoricalColsList))):\n", " RtData.boxplot(column='Rating', by=PredictorCol, figsize=(5,5), vert=True, ax=PlotCanvas[i])" ] }, { "cell_type": "markdown", "id": "ca28926c", "metadata": {}, "source": [ "In this data, all three categorical predictors looks correlated with the Target variable.\n", "\n", "We confirm this by looking at the results of ANOVA test below" ] }, { "cell_type": "markdown", "id": "cea7660f", "metadata": {}, "source": [ "## Statistical Feature Selection (Categorical Vs Continuous) using ANOVA test¶" ] }, { "cell_type": "markdown", "id": "33258606", "metadata": {}, "source": [ "Analysis of variance(ANOVA) is performed to check if there is any relationship between the given continuous and categorical variable\n", "\n", "Assumption(H0): There is NO relation between the given variables (i.e. The average(mean) values of the numeric Target variable is same for all the groups in the categorical Predictor variable)\n", "ANOVA Test result: Probability of H0 being true" ] }, { "cell_type": "code", "execution_count": 27, "id": "f165e879", "metadata": {}, "outputs": [], "source": [ "# Defining a function to find the statistical relationship with all the categorical variables\n", "def FunctionAnova(inpData, TargetVariable, CategoricalPredictorList):\n", " from scipy.stats import f_oneway\n", "\n", " # Creating an empty list of final selected predictors\n", " SelectedPredictors=[]\n", " \n", " print('##### ANOVA Results ##### \\n')\n", " for predictor in CategoricalPredictorList:\n", " CategoryGroupLists=inpData.groupby(predictor)[TargetVariable].apply(list)\n", " AnovaResults = f_oneway(*CategoryGroupLists)\n", " \n", " # If the ANOVA P-Value is <0.05, that means we reject H0\n", " if (AnovaResults[1] < 0.05):\n", " print(predictor, 'is correlated with', TargetVariable, '| P-Value:', AnovaResults[1])\n", " SelectedPredictors.append(predictor)\n", " else:\n", " print(predictor, 'is NOT correlated with', TargetVariable, '| P-Value:', AnovaResults[1])\n", " \n", " return(SelectedPredictors)" ] }, { "cell_type": "code", "execution_count": 28, "id": "e8d32462", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "##### ANOVA Results ##### \n", "\n", "Has Table booking is correlated with Rating | P-Value: 1.0038500766899416e-107\n", "Has Online delivery is correlated with Rating | P-Value: 3.4341981048863214e-164\n", "Price range is correlated with Rating | P-Value: 0.0\n" ] }, { "data": { "text/plain": [ "['Has Table booking', 'Has Online delivery', 'Price range']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calling the function to check which categorical variables are correlated with target\n", "CategoricalPredictorList=['Has Table booking', 'Has Online delivery', 'Price range']\n", "FunctionAnova(inpData=RtData, \n", " TargetVariable='Rating', \n", " CategoricalPredictorList=CategoricalPredictorList)" ] }, { "cell_type": "markdown", "id": "e9d9393f", "metadata": {}, "source": [ "The results of ANOVA confirm our visual analysis using box plots above.\n", "\n", "All categorical variables are correlated with the Target variable. This is something we guessed by looking at the box plots!\n", "\n", "Final selected Categorical columns:\n", "\n", "'Has Table booking', 'Has Online delivery', 'Price range'" ] }, { "cell_type": "markdown", "id": "4804593e", "metadata": {}, "source": [ "# Selecting final predictors for Machine Learning" ] }, { "cell_type": "markdown", "id": "48574116", "metadata": {}, "source": [ "Based on the above tests, selecting the final columns for machine learning" ] }, { "cell_type": "code", "execution_count": 29, "id": "5580036e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Votes Average Cost for two Has Table booking Has Online delivery \\\n", "624 140 850 No No \n", "625 71 700 No No \n", "626 94 500 No No \n", "627 87 400 No No \n", "628 177 1000 No No \n", "\n", " Price range \n", "624 3 \n", "625 2 \n", "626 2 \n", "627 2 \n", "628 3 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SelectedColumns=['Votes','Average Cost for two','Has Table booking',\n", " 'Has Online delivery','Price range']\n", "\n", "# Selecting final columns\n", "DataForML=RtData[SelectedColumns]\n", "DataForML.head()" ] }, { "cell_type": "markdown", "id": "c8a207a6", "metadata": {}, "source": [ "## Data Pre-processing for Machine Learning" ] }, { "cell_type": "markdown", "id": "2398894e", "metadata": {}, "source": [ "In this data there is no Ordinal categorical variable." ] }, { "cell_type": "markdown", "id": "085c49b0", "metadata": {}, "source": [ "## Converting the binary nominal variable to numeric using 1/0 mapping" ] }, { "cell_type": "code", "execution_count": 30, "id": "a74741c1", "metadata": {}, "outputs": [], "source": [ "# Converting the binary nominal variable sex to numeric\n", "DataForML['Has Table booking'].replace({'Yes':1, 'No':0}, inplace=True)\n", "DataForML['Has Online delivery'].replace({'Yes':1, 'No':0}, inplace=True)" ] }, { "cell_type": "markdown", "id": "ce49cb53", "metadata": {}, "source": [ "## Converting the nominal variable to numeric using get_dummies()" ] }, { "cell_type": "code", "execution_count": 31, "id": "64a19fc0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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VotesAverage Cost for twoHas Table bookingHas Online deliveryPrice rangeRating
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" ], "text/plain": [ " Votes Average Cost for two Has Table booking Has Online delivery \\\n", "624 140 850 0 0 \n", "625 71 700 0 0 \n", "626 94 500 0 0 \n", "627 87 400 0 0 \n", "628 177 1000 0 0 \n", "\n", " Price range Rating \n", "624 3 3.9 \n", "625 2 3.5 \n", "626 2 3.6 \n", "627 2 4.0 \n", "628 3 4.2 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Treating all the nominal variables at once using dummy variables\n", "DataForML_Numeric=pd.get_dummies(DataForML)\n", "\n", "# Adding Target Variable to the data\n", "DataForML_Numeric['Rating']=RtData['Rating']\n", "\n", "# Printing sample rows\n", "DataForML_Numeric.head()" ] }, { "cell_type": "markdown", "id": "61262704", "metadata": {}, "source": [ "# Machine Learning: Splitting the data into Training and Testing sample" ] }, { "cell_type": "markdown", "id": "41fdbfac", "metadata": {}, "source": [ "Typically 70% of data is used as Training data and the rest 30% is used as Tesing data." ] }, { "cell_type": "code", "execution_count": 32, "id": "b98ed60d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Votes', 'Average Cost for two', 'Has Table booking',\n", " 'Has Online delivery', 'Price range', 'Rating'],\n", " dtype='object')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Printing all the column names for our reference\n", "DataForML_Numeric.columns\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "bebcb2a6", "metadata": {}, "outputs": [], "source": [ "# Separate Target Variable and Predictor Variables\n", "TargetVariable='Rating'\n", "Predictors=['Votes', 'Average Cost for two', 'Has Table booking',\n", " 'Has Online delivery', 'Price range']\n", "\n", "X=DataForML_Numeric[Predictors].values\n", "y=DataForML_Numeric[TargetVariable].values\n", "\n", "# Split the data into training and testing set\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=428)" ] }, { "cell_type": "markdown", "id": "bcbab952", "metadata": {}, "source": [ "## Multiple Linear Regression" ] }, { "cell_type": "code", "execution_count": 34, "id": "3b33245f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LinearRegression()\n", "R2 Value: 0.2822477862429039\n", "\n", "##### Model Validation and Accuracy Calculations ##########\n", " Rating PredictedRating\n", "0 2.9 3.0\n", "1 3.7 3.0\n", "2 1.0 2.0\n", "3 1.0 3.0\n", "4 3.5 3.0\n", "Mean Accuracy on test data: 54.27566840471535\n", "Median Accuracy on test data: 73.17073170731707\n", "\n", "Accuracy values for 10-fold Cross Validation:\n", " [63.29631315 55.34985851 65.91701358 54.61968483 61.68027359 28.73432509\n", " 54.74405807 54.04180231 39.11057895 45.93544349]\n", "\n", "Final Average Accuracy of the model: 52.34\n" ] } ], "source": [ "# Multiple Linear Regression\n", "from sklearn.linear_model import LinearRegression\n", "RegModel = LinearRegression()\n", "\n", "# Printing all the parameters of Linear regression\n", "print(RegModel)\n", "\n", "# Creating the model on Training Data\n", "LREG=RegModel.fit(X_train,y_train)\n", "prediction=LREG.predict(X_test)\n", "\n", "# Taking the standardized values to original scale\n", "\n", "\n", "from sklearn import metrics\n", "# Measuring Goodness of fit in Training data\n", "print('R2 Value:',metrics.r2_score(y_train, LREG.predict(X_train)))\n", "\n", "###########################################################################\n", "print('\\n##### Model Validation and Accuracy Calculations ##########')\n", "\n", "# Printing some sample values of prediction\n", "TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)\n", "TestingDataResults[TargetVariable]=y_test\n", "TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)\n", "\n", "# Printing sample prediction values\n", "print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())\n", "\n", "# Calculating the error for each row\n", "TestingDataResults['APE']=100 * ((abs(\n", " TestingDataResults['Rating']-TestingDataResults['PredictedRating']))/TestingDataResults['Rating'])\n", "\n", "MAPE=np.mean(TestingDataResults['APE'])\n", "MedianMAPE=np.median(TestingDataResults['APE'])\n", "\n", "Accuracy =100 - MAPE\n", "MedianAccuracy=100- MedianMAPE\n", "print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier\n", "print('Median Accuracy on test data:', MedianAccuracy)\n", "\n", "\n", "# Defining a custom function to calculate accuracy\n", "# Make sure there are no zeros in the Target variable if you are using MAPE\n", "def Accuracy_Score(orig,pred):\n", " MAPE = np.mean(100 * (np.abs(orig-pred)/orig))\n", " #print('#'*70,'Accuracy:', 100-MAPE)\n", " return(100-MAPE)\n", "\n", "# Custom Scoring MAPE calculation\n", "from sklearn.metrics import make_scorer\n", "custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)\n", "\n", "# Importing cross validation function from sklearn\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Running 10-Fold Cross validation on a given algorithm\n", "# Passing full data X and y because the K-fold will split the data and automatically choose train/test\n", "Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)\n", "print('\\nAccuracy values for 10-fold Cross Validation:\\n',Accuracy_Values)\n", "print('\\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))" ] }, { "cell_type": "markdown", "id": "2fcffbde", "metadata": {}, "source": [ "## Decision Trees" ] }, { "cell_type": "code", "execution_count": 35, "id": "c97e5083", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DecisionTreeRegressor(max_depth=6)\n", "R2 Value: 0.9141248692925272\n", "\n", "##### Model Validation and Accuracy Calculations ##########\n", " Rating PredictedRating\n", "0 2.9 3.0\n", "1 3.7 3.0\n", "2 1.0 1.0\n", "3 1.0 1.0\n", "4 3.5 3.0\n", "Mean Accuracy on test data: 91.8409214775786\n", "Median Accuracy on test data: 93.75\n", "\n", "Accuracy values for 10-fold Cross Validation:\n", " [91.95971012 92.92280961 92.51311639 93.48579176 93.36576699 95.2695392\n", " 93.34562376 93.75536053 93.57415112 92.68280194]\n", "\n", "Final Average Accuracy of the model: 93.29\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Decision Trees (Multiple if-else statements!)\n", "from sklearn.tree import DecisionTreeRegressor\n", "RegModel = DecisionTreeRegressor(max_depth=6,criterion='mse')\n", "# Good Range of Max_depth = 2 to 20\n", "\n", "# Printing all the parameters of Decision Tree\n", "print(RegModel)\n", "\n", "# Creating the model on Training Data\n", "DT=RegModel.fit(X_train,y_train)\n", "prediction=DT.predict(X_test)\n", "\n", "from sklearn import metrics\n", "# Measuring Goodness of fit in Training data\n", "print('R2 Value:',metrics.r2_score(y_train, DT.predict(X_train)))\n", "\n", "# Plotting the feature importance for Top 10 most important columns\n", "%matplotlib inline\n", "feature_importances = pd.Series(DT.feature_importances_, index=Predictors)\n", "feature_importances.nlargest(10).plot(kind='barh')\n", "\n", "###########################################################################\n", "print('\\n##### Model Validation and Accuracy Calculations ##########')\n", "\n", "# Printing some sample values of prediction\n", "TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)\n", "TestingDataResults[TargetVariable]=y_test\n", "TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)\n", "\n", "# Printing sample prediction values\n", "print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())\n", "\n", "# Calculating the error for each row\n", "TestingDataResults['APE']=100 * ((abs(\n", " TestingDataResults['Rating']-TestingDataResults['PredictedRating']))/TestingDataResults['Rating'])\n", "\n", "MAPE=np.mean(TestingDataResults['APE'])\n", "MedianMAPE=np.median(TestingDataResults['APE'])\n", "\n", "Accuracy =100 - MAPE\n", "MedianAccuracy=100- MedianMAPE\n", "print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier\n", "print('Median Accuracy on test data:', MedianAccuracy)\n", "\n", "\n", "# Defining a custom function to calculate accuracy\n", "# Make sure there are no zeros in the Target variable if you are using MAPE\n", "def Accuracy_Score(orig,pred):\n", " MAPE = np.mean(100 * (np.abs(orig-pred)/orig))\n", " #print('#'*70,'Accuracy:', 100-MAPE)\n", " return(100-MAPE)\n", "\n", "# Custom Scoring MAPE calculation\n", "from sklearn.metrics import make_scorer\n", "custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)\n", "\n", "# Importing cross validation function from sklearn\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Running 10-Fold Cross validation on a given algorithm\n", "# Passing full data X and y because the K-fold will split the data and automatically choose train/test\n", "Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)\n", "print('\\nAccuracy values for 10-fold Cross Validation:\\n',Accuracy_Values)\n", "print('\\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))" ] }, { "cell_type": "code", "execution_count": 36, "id": "95152103", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Plotting the Decision Tree\n", "from sklearn import tree\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# Setting dpi = 300 to make image clearer than default\n", "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n", "\n", "tree.plot_tree(RegModel,feature_names=Predictors,class_names=TargetVariable,filled = True);\n" ] }, { "cell_type": "markdown", "id": "f8df7c0f", "metadata": {}, "source": [ "It is hard to visualize the decision tree as it is pretty huge." ] }, { "cell_type": "markdown", "id": "348e14e3", "metadata": {}, "source": [ "## Random Forest" ] }, { "cell_type": "code", "execution_count": 37, "id": "6151d30c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RandomForestRegressor(max_depth=2, n_estimators=400)\n", "R2 Value: 0.8943867721024563\n", "\n", "##### Model Validation and Accuracy Calculations ##########\n", " Rating PredictedRating\n", "0 2.9 3.0\n", "1 3.7 3.0\n", "2 1.0 1.0\n", "3 1.0 1.0\n", "4 3.5 4.0\n", "Mean Accuracy on test data: 91.61895662647301\n", "Median Accuracy on test data: 93.75\n", "\n", "Accuracy values for 10-fold Cross Validation:\n", " [90.97725832 92.06985756 91.66206944 92.7406027 92.68782668 94.52208395\n", " 92.48665071 92.75477037 92.6387014 91.90550426]\n", "\n", "Final Average Accuracy of the model: 92.44\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Random Forest (Bagging of multiple Decision Trees)\n", "from sklearn.ensemble import RandomForestRegressor\n", "RegModel = RandomForestRegressor(max_depth=2, n_estimators=400,criterion='mse')\n", "# Good range for max_depth: 2-10 and n_estimators: 100-1000\n", "\n", "# Printing all the parameters of Random Forest\n", "print(RegModel)\n", "\n", "# Creating the model on Training Data\n", "RF=RegModel.fit(X_train,y_train)\n", "prediction=RF.predict(X_test)\n", "\n", "from sklearn import metrics\n", "# Measuring Goodness of fit in Training data\n", "print('R2 Value:',metrics.r2_score(y_train, RF.predict(X_train)))\n", "\n", "# Plotting the feature importance for Top 10 most important columns\n", "%matplotlib inline\n", "feature_importances = pd.Series(RF.feature_importances_, index=Predictors)\n", "feature_importances.nlargest(10).plot(kind='barh')\n", "\n", "###########################################################################\n", "print('\\n##### Model Validation and Accuracy Calculations ##########')\n", "\n", "# Printing some sample values of prediction\n", "TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)\n", "TestingDataResults[TargetVariable]=y_test\n", "TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)\n", "\n", "# Printing sample prediction values\n", "print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())\n", "\n", "# Calculating the error for each row\n", "TestingDataResults['APE']=100 * ((abs(\n", " TestingDataResults['Rating']-TestingDataResults['PredictedRating']))/TestingDataResults['Rating'])\n", "\n", "MAPE=np.mean(TestingDataResults['APE'])\n", "MedianMAPE=np.median(TestingDataResults['APE'])\n", "\n", "Accuracy =100 - MAPE\n", "MedianAccuracy=100- MedianMAPE\n", "print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier\n", "print('Median Accuracy on test data:', MedianAccuracy)\n", "\n", "\n", "# Defining a custom function to calculate accuracy\n", "# Make sure there are no zeros in the Target variable if you are using MAPE\n", "def Accuracy_Score(orig,pred):\n", " MAPE = np.mean(100 * (np.abs(orig-pred)/orig))\n", " #print('#'*70,'Accuracy:', 100-MAPE)\n", " return(100-MAPE)\n", "\n", "# Custom Scoring MAPE calculation\n", "from sklearn.metrics import make_scorer\n", "custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)\n", "\n", "# Importing cross validation function from sklearn\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Running 10-Fold Cross validation on a given algorithm\n", "# Passing full data X and y because the K-fold will split the data and automatically choose train/test\n", "Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)\n", "print('\\nAccuracy values for 10-fold Cross Validation:\\n',Accuracy_Values)\n", "print('\\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))" ] }, { "cell_type": "code", "execution_count": null, "id": "112320d5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "f9333848", "metadata": {}, "source": [ "## XGBoost Model" ] }, { "cell_type": "code", "execution_count": 38, "id": "1a65154f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XGBRegressor(base_score=None, booster='gbtree', callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, gamma=None,\n", " gpu_id=None, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=0.1, max_bin=None,\n", " max_cat_to_onehot=None, max_delta_step=None, max_depth=2,\n", " max_leaves=None, min_child_weight=None, missing=nan,\n", " monotone_constraints=None, n_estimators=1000, n_jobs=None,\n", " num_parallel_tree=None, objective='reg:linear', predictor=None,\n", " random_state=None, reg_alpha=None, ...)\n", "R2 Value: 0.9250152884865178\n", "\n", "##### Model Validation and Accuracy Calculations ##########\n", " Rating PredictedRating\n", "0 2.9 3.0\n", "1 3.7 3.0\n", "2 1.0 1.0\n", "3 1.0 1.0\n", "4 3.5 3.0\n", "Mean Accuracy on test data: 91.64145441195488\n", "Median Accuracy on test data: 93.75\n", "\n", "Accuracy values for 10-fold Cross Validation:\n", " [92.07203702 92.68395859 92.24115355 93.11667709 93.21490297 94.96045914\n", " 93.19251181 93.43370582 93.22015639 92.37322535]\n", "\n", "Final Average Accuracy of the model: 93.05\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Xtreme Gradient Boosting (XGBoost)\n", "from xgboost import XGBRegressor\n", "RegModel=XGBRegressor(max_depth=2,learning_rate=0.1, verbosity = 0, silent=True, n_estimators=1000, objective='reg:linear',\n", " booster='gbtree')\n", "\n", "# Printing all the parameters of XGBoost\n", "print(RegModel)\n", "\n", "# Creating the model on Training Data\n", "XGB=RegModel.fit(X_train,y_train)\n", "prediction=XGB.predict(X_test)\n", "\n", "from sklearn import metrics\n", "# Measuring Goodness of fit in Training data\n", "print('R2 Value:',metrics.r2_score(y_train, XGB.predict(X_train)))\n", "\n", "# Plotting the feature importance for Top 10 most important columns\n", "%matplotlib inline\n", "feature_importances = pd.Series(XGB.feature_importances_, index=Predictors)\n", "feature_importances.nlargest(10).plot(kind='barh')\n", "###########################################################################\n", "print('\\n##### Model Validation and Accuracy Calculations ##########')\n", "\n", "# Printing some sample values of prediction\n", "TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors)\n", "TestingDataResults[TargetVariable]=y_test\n", "TestingDataResults[('Predicted'+TargetVariable)]=np.round(prediction)\n", "\n", "# Printing sample prediction values\n", "print(TestingDataResults[[TargetVariable,'Predicted'+TargetVariable]].head())\n", "\n", "# Calculating the error for each row\n", "TestingDataResults['APE']=100 * ((abs(\n", " TestingDataResults['Rating']-TestingDataResults['PredictedRating']))/TestingDataResults['Rating'])\n", "\n", "\n", "MAPE=np.mean(TestingDataResults['APE'])\n", "MedianMAPE=np.median(TestingDataResults['APE'])\n", "\n", "Accuracy =100 - MAPE\n", "MedianAccuracy=100- MedianMAPE\n", "print('Mean Accuracy on test data:', Accuracy) # Can be negative sometimes due to outlier\n", "print('Median Accuracy on test data:', MedianAccuracy)\n", "\n", "\n", "# Defining a custom function to calculate accuracy\n", "# Make sure there are no zeros in the Target variable if you are using MAPE\n", "def Accuracy_Score(orig,pred):\n", " MAPE = np.mean(100 * (np.abs(orig-pred)/orig))\n", " #print('#'*70,'Accuracy:', 100-MAPE)\n", " return(100-MAPE)\n", "\n", "# Custom Scoring MAPE calculation\n", "from sklearn.metrics import make_scorer\n", "custom_Scoring=make_scorer(Accuracy_Score, greater_is_better=True)\n", "\n", "# Importing cross validation function from sklearn\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Running 10-Fold Cross validation on a given algorithm\n", "# Passing full data X and y because the K-fold will split the data and automatically choose train/test\n", "Accuracy_Values=cross_val_score(RegModel, X , y, cv=10, scoring=custom_Scoring)\n", "print('\\nAccuracy values for 10-fold Cross Validation:\\n',Accuracy_Values)\n", "print('\\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))" ] }, { "cell_type": "markdown", "id": "eaf19a95", "metadata": {}, "source": [ "# Deployment of the Model" ] }, { "cell_type": "markdown", "id": "38e27b27", "metadata": {}, "source": [ "Based on the above trials you select that algorithm which produces the best average accuracy. In this case, multiple algorithms have produced similar kind of average accuracy. Hence, we can choose any one of them.\n", "\n", "I am choosing \"XGBoost Algorithm\" as the final model for predicting the ratings since it has highest accuracy and it also tries to utilize other features.Further, I have deployed this model by creating a streamlit application which has been put in a seperate file.\n", "\n", "Streamlit is an open source app framework in Python language. It helps us create web apps for data science and machine learning projects.\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }