| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "id": "8de49094", | |
| "metadata": {}, | |
| "source": [ | |
| "# Machine Learning November Minor Project\n", | |
| "## create a classification model to predict whether price range of mobile based on certain specification" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 132, | |
| "id": "9229995e", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd \n", | |
| "import matplotlib.pyplot as plt" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 133, | |
| "id": "14ea8a12", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>battery_power</th>\n", | |
| " <th>blue</th>\n", | |
| " <th>clock_speed</th>\n", | |
| " <th>dual_sim</th>\n", | |
| " <th>fc</th>\n", | |
| " <th>four_g</th>\n", | |
| " <th>int_memory</th>\n", | |
| " <th>m_dep</th>\n", | |
| " <th>mobile_wt</th>\n", | |
| " <th>n_cores</th>\n", | |
| " <th>...</th>\n", | |
| " <th>px_height</th>\n", | |
| " <th>px_width</th>\n", | |
| " <th>ram</th>\n", | |
| " <th>sc_h</th>\n", | |
| " <th>sc_w</th>\n", | |
| " <th>talk_time</th>\n", | |
| " <th>three_g</th>\n", | |
| " <th>touch_screen</th>\n", | |
| " <th>wifi</th>\n", | |
| " <th>price_range</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>842</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2.2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>188</td>\n", | |
| " <td>2</td>\n", | |
| " <td>...</td>\n", | |
| " <td>20</td>\n", | |
| " <td>756</td>\n", | |
| " <td>2549</td>\n", | |
| " <td>9</td>\n", | |
| " <td>7</td>\n", | |
| " <td>19</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1021</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>53</td>\n", | |
| " <td>0.7</td>\n", | |
| " <td>136</td>\n", | |
| " <td>3</td>\n", | |
| " <td>...</td>\n", | |
| " <td>905</td>\n", | |
| " <td>1988</td>\n", | |
| " <td>2631</td>\n", | |
| " <td>17</td>\n", | |
| " <td>3</td>\n", | |
| " <td>7</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>563</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " <td>41</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>145</td>\n", | |
| " <td>5</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1263</td>\n", | |
| " <td>1716</td>\n", | |
| " <td>2603</td>\n", | |
| " <td>11</td>\n", | |
| " <td>2</td>\n", | |
| " <td>9</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>615</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0.8</td>\n", | |
| " <td>131</td>\n", | |
| " <td>6</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1216</td>\n", | |
| " <td>1786</td>\n", | |
| " <td>2769</td>\n", | |
| " <td>16</td>\n", | |
| " <td>8</td>\n", | |
| " <td>11</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1821</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1.2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>13</td>\n", | |
| " <td>1</td>\n", | |
| " <td>44</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>141</td>\n", | |
| " <td>2</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1208</td>\n", | |
| " <td>1212</td>\n", | |
| " <td>1411</td>\n", | |
| " <td>8</td>\n", | |
| " <td>2</td>\n", | |
| " <td>15</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1995</th>\n", | |
| " <td>794</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0.8</td>\n", | |
| " <td>106</td>\n", | |
| " <td>6</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1222</td>\n", | |
| " <td>1890</td>\n", | |
| " <td>668</td>\n", | |
| " <td>13</td>\n", | |
| " <td>4</td>\n", | |
| " <td>19</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1996</th>\n", | |
| " <td>1965</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2.6</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>39</td>\n", | |
| " <td>0.2</td>\n", | |
| " <td>187</td>\n", | |
| " <td>4</td>\n", | |
| " <td>...</td>\n", | |
| " <td>915</td>\n", | |
| " <td>1965</td>\n", | |
| " <td>2032</td>\n", | |
| " <td>11</td>\n", | |
| " <td>10</td>\n", | |
| " <td>16</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1997</th>\n", | |
| " <td>1911</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>36</td>\n", | |
| " <td>0.7</td>\n", | |
| " <td>108</td>\n", | |
| " <td>8</td>\n", | |
| " <td>...</td>\n", | |
| " <td>868</td>\n", | |
| " <td>1632</td>\n", | |
| " <td>3057</td>\n", | |
| " <td>9</td>\n", | |
| " <td>1</td>\n", | |
| " <td>5</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1998</th>\n", | |
| " <td>1512</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>0</td>\n", | |
| " <td>4</td>\n", | |
| " <td>1</td>\n", | |
| " <td>46</td>\n", | |
| " <td>0.1</td>\n", | |
| " <td>145</td>\n", | |
| " <td>5</td>\n", | |
| " <td>...</td>\n", | |
| " <td>336</td>\n", | |
| " <td>670</td>\n", | |
| " <td>869</td>\n", | |
| " <td>18</td>\n", | |
| " <td>10</td>\n", | |
| " <td>19</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1999</th>\n", | |
| " <td>510</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>5</td>\n", | |
| " <td>1</td>\n", | |
| " <td>45</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>168</td>\n", | |
| " <td>6</td>\n", | |
| " <td>...</td>\n", | |
| " <td>483</td>\n", | |
| " <td>754</td>\n", | |
| " <td>3919</td>\n", | |
| " <td>19</td>\n", | |
| " <td>4</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>2000 rows × 21 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " battery_power blue clock_speed dual_sim fc four_g int_memory \\\n", | |
| "0 842 0 2.2 0 1 0 7 \n", | |
| "1 1021 1 0.5 1 0 1 53 \n", | |
| "2 563 1 0.5 1 2 1 41 \n", | |
| "3 615 1 2.5 0 0 0 10 \n", | |
| "4 1821 1 1.2 0 13 1 44 \n", | |
| "... ... ... ... ... .. ... ... \n", | |
| "1995 794 1 0.5 1 0 1 2 \n", | |
| "1996 1965 1 2.6 1 0 0 39 \n", | |
| "1997 1911 0 0.9 1 1 1 36 \n", | |
| "1998 1512 0 0.9 0 4 1 46 \n", | |
| "1999 510 1 2.0 1 5 1 45 \n", | |
| "\n", | |
| " m_dep mobile_wt n_cores ... px_height px_width ram sc_h sc_w \\\n", | |
| "0 0.6 188 2 ... 20 756 2549 9 7 \n", | |
| "1 0.7 136 3 ... 905 1988 2631 17 3 \n", | |
| "2 0.9 145 5 ... 1263 1716 2603 11 2 \n", | |
| "3 0.8 131 6 ... 1216 1786 2769 16 8 \n", | |
| "4 0.6 141 2 ... 1208 1212 1411 8 2 \n", | |
| "... ... ... ... ... ... ... ... ... ... \n", | |
| "1995 0.8 106 6 ... 1222 1890 668 13 4 \n", | |
| "1996 0.2 187 4 ... 915 1965 2032 11 10 \n", | |
| "1997 0.7 108 8 ... 868 1632 3057 9 1 \n", | |
| "1998 0.1 145 5 ... 336 670 869 18 10 \n", | |
| "1999 0.9 168 6 ... 483 754 3919 19 4 \n", | |
| "\n", | |
| " talk_time three_g touch_screen wifi price_range \n", | |
| "0 19 0 0 1 1 \n", | |
| "1 7 1 1 0 2 \n", | |
| "2 9 1 1 0 2 \n", | |
| "3 11 1 0 0 2 \n", | |
| "4 15 1 1 0 1 \n", | |
| "... ... ... ... ... ... \n", | |
| "1995 19 1 1 0 0 \n", | |
| "1996 16 1 1 1 2 \n", | |
| "1997 5 1 1 0 3 \n", | |
| "1998 19 1 1 1 0 \n", | |
| "1999 2 1 1 1 3 \n", | |
| "\n", | |
| "[2000 rows x 21 columns]" | |
| ] | |
| }, | |
| "execution_count": 133, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df=pd.read_csv(\"MOBILE.csv\")\n", | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 134, | |
| "id": "3502e202", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>battery_power</th>\n", | |
| " <th>blue</th>\n", | |
| " <th>clock_speed</th>\n", | |
| " <th>dual_sim</th>\n", | |
| " <th>fc</th>\n", | |
| " <th>four_g</th>\n", | |
| " <th>int_memory</th>\n", | |
| " <th>m_dep</th>\n", | |
| " <th>mobile_wt</th>\n", | |
| " <th>n_cores</th>\n", | |
| " <th>...</th>\n", | |
| " <th>px_height</th>\n", | |
| " <th>px_width</th>\n", | |
| " <th>ram</th>\n", | |
| " <th>sc_h</th>\n", | |
| " <th>sc_w</th>\n", | |
| " <th>talk_time</th>\n", | |
| " <th>three_g</th>\n", | |
| " <th>touch_screen</th>\n", | |
| " <th>wifi</th>\n", | |
| " <th>price_range</th>\n", | |
| " </tr>\n", | |
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| " <th>0</th>\n", | |
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| " <td>7</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>188</td>\n", | |
| " <td>2</td>\n", | |
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| " <td>1021</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>53</td>\n", | |
| " <td>0.7</td>\n", | |
| " <td>136</td>\n", | |
| " <td>3</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>563</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.5</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " <td>41</td>\n", | |
| " <td>0.9</td>\n", | |
| " <td>145</td>\n", | |
| " <td>5</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1263</td>\n", | |
| " <td>1716</td>\n", | |
| " <td>2603</td>\n", | |
| " <td>11</td>\n", | |
| " <td>2</td>\n", | |
| " <td>9</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>615</td>\n", | |
| " <td>1</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0.8</td>\n", | |
| " <td>131</td>\n", | |
| " <td>6</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1216</td>\n", | |
| " <td>1786</td>\n", | |
| " <td>2769</td>\n", | |
| " <td>16</td>\n", | |
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| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1821</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1.2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>13</td>\n", | |
| " <td>1</td>\n", | |
| " <td>44</td>\n", | |
| " <td>0.6</td>\n", | |
| " <td>141</td>\n", | |
| " <td>2</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1208</td>\n", | |
| " <td>1212</td>\n", | |
| " <td>1411</td>\n", | |
| " <td>8</td>\n", | |
| " <td>2</td>\n", | |
| " <td>15</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 21 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " battery_power blue clock_speed dual_sim fc four_g int_memory m_dep \\\n", | |
| "0 842 0 2.2 0 1 0 7 0.6 \n", | |
| "1 1021 1 0.5 1 0 1 53 0.7 \n", | |
| "2 563 1 0.5 1 2 1 41 0.9 \n", | |
| "3 615 1 2.5 0 0 0 10 0.8 \n", | |
| "4 1821 1 1.2 0 13 1 44 0.6 \n", | |
| "\n", | |
| " mobile_wt n_cores ... px_height px_width ram sc_h sc_w talk_time \\\n", | |
| "0 188 2 ... 20 756 2549 9 7 19 \n", | |
| "1 136 3 ... 905 1988 2631 17 3 7 \n", | |
| "2 145 5 ... 1263 1716 2603 11 2 9 \n", | |
| "3 131 6 ... 1216 1786 2769 16 8 11 \n", | |
| "4 141 2 ... 1208 1212 1411 8 2 15 \n", | |
| "\n", | |
| " three_g touch_screen wifi price_range \n", | |
| "0 0 0 1 1 \n", | |
| "1 1 1 0 2 \n", | |
| "2 1 1 0 2 \n", | |
| "3 1 0 0 2 \n", | |
| "4 1 1 0 1 \n", | |
| "\n", | |
| "[5 rows x 21 columns]" | |
| ] | |
| }, | |
| "execution_count": 134, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 135, | |
| "id": "3494f177", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(2000, 21)" | |
| ] | |
| }, | |
| "execution_count": 135, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "583f8b35", | |
| "metadata": {}, | |
| "source": [ | |
| "TARGET VARIABLE" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 136, | |
| "id": "81466eab", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "1 500\n", | |
| "2 500\n", | |
| "3 500\n", | |
| "0 500\n", | |
| "Name: price_range, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 136, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df['price_range'].value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "f89ae5f7", | |
| "metadata": {}, | |
| "source": [ | |
| "# 1) REMOVE HANDLE NULL VALUES(IF ANY)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 137, | |
| "id": "67af79d5", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "battery_power 0\n", | |
| "blue 0\n", | |
| "clock_speed 0\n", | |
| "dual_sim 0\n", | |
| "fc 0\n", | |
| "four_g 0\n", | |
| "int_memory 0\n", | |
| "m_dep 0\n", | |
| "mobile_wt 0\n", | |
| "n_cores 0\n", | |
| "pc 0\n", | |
| "px_height 0\n", | |
| "px_width 0\n", | |
| "ram 0\n", | |
| "sc_h 0\n", | |
| "sc_w 0\n", | |
| "talk_time 0\n", | |
| "three_g 0\n", | |
| "touch_screen 0\n", | |
| "wifi 0\n", | |
| "price_range 0\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 137, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.isnull().sum()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "2a9229f6", | |
| "metadata": {}, | |
| "source": [ | |
| "HANDLING DUPLICATES" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 138, | |
| "id": "68e9c1a0", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0" | |
| ] | |
| }, | |
| "execution_count": 138, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.duplicated().sum()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 139, | |
| "id": "45b814b1", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df.drop_duplicates(inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 140, | |
| "id": "86e353c5", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0" | |
| ] | |
| }, | |
| "execution_count": 140, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.duplicated().sum()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "ae3fb359", | |
| "metadata": {}, | |
| "source": [ | |
| "CHECKING DATATYPES" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 141, | |
| "id": "4206a483", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "battery_power int64\n", | |
| "blue int64\n", | |
| "clock_speed float64\n", | |
| "dual_sim int64\n", | |
| "fc int64\n", | |
| "four_g int64\n", | |
| "int_memory int64\n", | |
| "m_dep float64\n", | |
| "mobile_wt int64\n", | |
| "n_cores int64\n", | |
| "pc int64\n", | |
| "px_height int64\n", | |
| "px_width int64\n", | |
| "ram int64\n", | |
| "sc_h int64\n", | |
| "sc_w int64\n", | |
| "talk_time int64\n", | |
| "three_g int64\n", | |
| "touch_screen int64\n", | |
| "wifi int64\n", | |
| "price_range int64\n", | |
| "dtype: object" | |
| ] | |
| }, | |
| "execution_count": 141, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.dtypes" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "27c38f40", | |
| "metadata": {}, | |
| "source": [ | |
| "#selecting dependent(x) and independent(y) variables" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 142, | |
| "id": "df81170a", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "<class 'pandas.core.series.Series'>\n", | |
| "(2000, 20)\n", | |
| "(2000,)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "x=df.drop('price_range',axis=1)\n", | |
| "y=df['price_range']\n", | |
| "print(type(x))\n", | |
| "print(type(y))\n", | |
| "print(x.shape)\n", | |
| "print(y.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "caddcb4b", | |
| "metadata": {}, | |
| "source": [ | |
| "x=df.drop('price_range',axis=1)\n", | |
| "y=df['price_range']\n", | |
| "print(type(x))\n", | |
| "print(type(y))\n", | |
| "print(x.shape)\n", | |
| "print(y.shape)\n", | |
| "#x-tarin , y_train" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "50e080a5", | |
| "metadata": {}, | |
| "source": [ | |
| "# #2) spliting data into training and test data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 143, | |
| "id": "0a2e0e9c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.model_selection import train_test_split" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 144, | |
| "id": "e72f938f", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "500.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(2000*0.25)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 145, | |
| "id": "e79d1a83", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(1500, 20)\n", | |
| "(500, 20)\n", | |
| "(1500,)\n", | |
| "(500,)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=42)\n", | |
| "print(x_train.shape)\n", | |
| "print(x_test.shape)\n", | |
| "print(y_train.shape)\n", | |
| "print(y_test.shape) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "5b530940", | |
| "metadata": {}, | |
| "source": [ | |
| "### CONFUSION MATRIX" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 146, | |
| "id": "aa92457c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix,classification_report\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 147, | |
| "id": "82b8c07b", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def eval_model(ytest,ypred):\n", | |
| " cm=confusion_matrix(ytest,ypred)\n", | |
| " print(cm)\n", | |
| " print(classification_report(ytest,ypred))\n", | |
| "def nscore(model):\n", | |
| " print('training score', model.score(x_train,y_train))\n", | |
| " print('testing score',model.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "51d24954", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "95fea4e2", | |
| "metadata": {}, | |
| "source": [ | |
| "# #3 Apply the following models on the training dataset and generate the predicted value for the test dataset\n", | |
| "\n", | |
| "# (a) LOGISTIC REGRESSION\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 148, | |
| "id": "21ec5184", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 149, | |
| "id": "bd982d05", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.linear_model import LogisticRegression " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 150, | |
| "id": "e76e207e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "LogisticRegression(max_iter=10000, solver='liblinear')" | |
| ] | |
| }, | |
| "execution_count": 150, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| " #applying linear regression\n", | |
| "m1=LogisticRegression(max_iter=10000,solver=\"liblinear\")\n", | |
| "m1.fit(x_train,y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 151, | |
| "id": "b9aef55f", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.7946666666666666\n", | |
| "train score 0.782\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Accuracy\n", | |
| "print('train score',m1.score(x_train,y_train))\n", | |
| "print('train score',m1.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 152, | |
| "id": "dddc5c20", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "training score 0.7946666666666666\n", | |
| "testing score 0.782\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "nscore(m1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 153, | |
| "id": "986ee64d", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[0 2 0 3 1 2 2 0 3 1 0 1 1 3 3 2 3 3 1 0 0 1 2 2 0 2 3 3 2 0 0 0 3 0 2 1 2\n", | |
| " 0 3 0 2 3 3 0 2 3 1 1 3 1 3 1 0 0 1 2 2 3 0 0 1 3 3 2 1 0 3 3 2 2 2 1 0 1\n", | |
| " 3 0 1 3 1 1 3 1 2 0 1 3 2 3 3 0 3 3 2 1 3 2 2 3 2 1 0 0 1 0 0 3 2 0 2 1 0\n", | |
| " 0 3 1 3 2 3 3 0 2 1 3 3 2 3 3 0 3 0 2 3 0 1 3 0 3 1 0 0 2 3 1 3 3 0 0 0 2\n", | |
| " 2 2 3 1 1 0 2 3 0 1 0 1 2 3 3 1 1 0 0 2 2 3 3 0 0 0 3 1 2 2 1 0 0 0 0 0 3\n", | |
| " 2 0 3 0 0 0 0 1 3 3 1 0 1 2 0 1 2 1 3 3 3 1 2 0 0 0 1 1 1 3 1 1 2 1 1 3 1\n", | |
| " 3 0 0 2 0 3 0 0 1 0 1 3 2 1 1 2 3 0 2 3 2 3 0 3 1 3 3 3 2 1 0 3 3 1 3 3 3\n", | |
| " 3 3 0 1 2 3 2 3 0 2 3 2 3 2 0 0 2 0 3 3 1 3 2 0 3 1 2 0 0 3 0 1 2 3 3 3 0\n", | |
| " 1 0 0 3 3 0 1 2 2 0 3 3 2 3 1 3 3 0 2 1 2 2 0 0 0 3 3 3 1 0 1 0 2 3 2 0 2\n", | |
| " 3 2 1 3 0 0 3 1 3 1 0 1 1 2 1 2 3 1 0 1 2 3 0 3 0 0 1 0 2 2 2 2 3 0 3 2 3\n", | |
| " 3 3 3 3 1 2 0 3 2 3 3 0 2 3 1 3 3 3 1 0 2 3 0 0 2 3 2 1 2 2 1 3 0 3 1 3 0\n", | |
| " 0 1 0 1 0 2 0 2 3 3 1 2 1 3 1 1 3 1 0 0 3 0 2 0 0 2 3 3 0 2 0 1 2 3 3 0 3\n", | |
| " 0 2 0 0 3 3 0 2 1 2 3 2 1 0 1 3 1 0 3 1 0 0 3 2 3 2 0 3 2 0 1 2 3 2 1 0 0\n", | |
| " 0 2 3 1 0 2 3 1 3 1 2 2 3 0 0 1 2 3 1]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m1=m1.predict(x_test)\n", | |
| "print(ypred_m1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 154, | |
| "id": "8ed82313", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[124 7 1 0]\n", | |
| " [ 13 72 33 0]\n", | |
| " [ 0 25 69 26]\n", | |
| " [ 0 0 4 126]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.91 0.94 0.92 132\n", | |
| " 1 0.69 0.61 0.65 118\n", | |
| " 2 0.64 0.57 0.61 120\n", | |
| " 3 0.83 0.97 0.89 130\n", | |
| "\n", | |
| " accuracy 0.78 500\n", | |
| " macro avg 0.77 0.77 0.77 500\n", | |
| "weighted avg 0.77 0.78 0.77 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m1=m1.predict(x_test)\n", | |
| "eval_model(y_test,ypred_m1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 155, | |
| "id": "a309d30d", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix,classification_report" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 156, | |
| "id": "8c470b57", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[124 7 1 0]\n", | |
| " [ 13 72 33 0]\n", | |
| " [ 0 25 69 26]\n", | |
| " [ 0 0 4 126]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.91 0.94 0.92 132\n", | |
| " 1 0.69 0.61 0.65 118\n", | |
| " 2 0.64 0.57 0.61 120\n", | |
| " 3 0.83 0.97 0.89 130\n", | |
| "\n", | |
| " accuracy 0.78 500\n", | |
| " macro avg 0.77 0.77 0.77 500\n", | |
| "weighted avg 0.77 0.78 0.77 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(confusion_matrix(y_test,ypred_m1))\n", | |
| "print(classification_report(y_test,ypred_m1))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 157, | |
| "id": "2899dae3", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.782\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('train score',m1.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "7c414d0b", | |
| "metadata": {}, | |
| "source": [ | |
| "# (b)KNN classification" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 158, | |
| "id": "15b82df3", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.neighbors import KNeighborsClassifier\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 159, | |
| "id": "43b4899b", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "KNeighborsClassifier(n_neighbors=11)" | |
| ] | |
| }, | |
| "execution_count": 159, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "m2=KNeighborsClassifier(n_neighbors=11)\n", | |
| "m2.fit(x_train,y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 160, | |
| "id": "1def1105", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.952\n", | |
| "train score 0.938\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Accuracy\n", | |
| "print('train score',m2.score(x_train,y_train))\n", | |
| "print('train score',m2.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 161, | |
| "id": "8ed4a6e8", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[0 2 1 3 1 1 2 0 3 1 0 1 2 3 2 2 3 3 1 0 0 1 1 2 0 1 3 2 2 0 0 0 3 0 1 1 2\n", | |
| " 0 3 0 2 2 2 0 3 2 2 1 3 1 3 1 0 0 0 1 1 3 0 0 1 3 3 1 0 0 3 3 1 2 2 2 0 1\n", | |
| " 2 0 0 3 2 1 3 2 1 0 1 3 1 3 3 0 3 3 2 1 3 2 2 3 1 1 0 0 1 0 0 3 2 0 1 1 0\n", | |
| " 0 3 1 3 2 3 2 0 2 1 3 2 1 3 3 0 2 0 2 3 0 2 2 0 3 1 0 0 2 2 1 2 2 0 0 0 1\n", | |
| " 1 2 3 1 1 0 2 2 0 1 0 2 2 3 3 2 1 0 1 2 2 3 3 0 1 0 3 1 1 2 1 0 0 0 0 0 3\n", | |
| " 2 0 3 0 0 0 0 1 3 3 1 0 1 1 1 1 1 2 3 3 3 1 2 0 0 0 2 1 1 3 1 0 2 1 1 3 2\n", | |
| " 3 0 0 2 1 3 0 1 2 0 2 3 2 0 1 3 3 0 1 3 2 3 0 3 1 2 3 3 2 1 1 3 3 1 3 3 3\n", | |
| " 3 3 0 2 2 2 1 3 0 1 3 2 2 2 1 0 1 0 3 3 1 3 1 0 3 1 2 0 0 3 0 1 2 3 3 3 1\n", | |
| " 1 0 1 3 3 0 1 2 2 0 3 3 2 3 2 3 2 0 2 1 1 1 0 0 0 3 2 3 1 0 1 0 1 2 3 0 3\n", | |
| " 3 2 1 2 0 0 2 1 3 2 0 1 1 1 0 1 3 2 0 0 3 3 0 3 0 0 2 0 1 2 2 2 3 0 3 2 2\n", | |
| " 3 3 3 2 1 1 0 3 1 3 3 0 2 3 2 3 3 3 0 0 2 3 0 0 2 3 2 1 1 2 1 2 1 3 1 2 0\n", | |
| " 0 1 0 1 0 1 0 2 2 3 2 1 1 3 1 0 3 1 0 0 3 0 1 0 0 1 3 3 0 2 0 1 1 3 3 1 2\n", | |
| " 0 2 0 0 3 3 0 2 2 1 3 1 2 0 1 3 1 0 3 1 0 0 3 2 3 2 0 2 1 0 1 2 3 2 1 1 0\n", | |
| " 1 2 2 1 1 1 3 1 2 0 2 2 3 1 0 1 2 3 1]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m2=m2.predict(x_test)\n", | |
| "print(ypred_m2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 162, | |
| "id": "30329e38", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix,classification_report" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 163, | |
| "id": "676b2ec2", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[127 5 0 0]\n", | |
| " [ 4 113 1 0]\n", | |
| " [ 0 11 106 3]\n", | |
| " [ 0 0 7 123]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.97 0.96 0.97 132\n", | |
| " 1 0.88 0.96 0.91 118\n", | |
| " 2 0.93 0.88 0.91 120\n", | |
| " 3 0.98 0.95 0.96 130\n", | |
| "\n", | |
| " accuracy 0.94 500\n", | |
| " macro avg 0.94 0.94 0.94 500\n", | |
| "weighted avg 0.94 0.94 0.94 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(confusion_matrix(y_test,ypred_m2))\n", | |
| "print(classification_report(y_test,ypred_m2))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 164, | |
| "id": "fc497909", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.938\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('train score',m2.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "c99e4956", | |
| "metadata": {}, | |
| "source": [ | |
| "# # (c) SVM classifier with linear and rbf kernal\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 165, | |
| "id": "9bf791f9", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.svm import SVC" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 166, | |
| "id": "b65a6b55", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "m3=SVC(kernel='linear')\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 167, | |
| "id": "d459ed8e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "SVC(kernel='linear')" | |
| ] | |
| }, | |
| "execution_count": 167, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "m3.fit(x_train,y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 168, | |
| "id": "f77d04cd", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "training score 0.992\n", | |
| "testing score 0.97\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "nscore(m3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 169, | |
| "id": "03c26eff", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.992\n", | |
| "train score 0.97\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Accuracy\n", | |
| "print('train score',m3.score(x_train,y_train))\n", | |
| "print('train score',m3.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 170, | |
| "id": "c9f4fdd1", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[0 2 1 3 1 1 2 0 3 1 0 1 2 3 3 2 3 3 1 0 0 2 1 2 0 1 3 2 2 0 0 0 3 0 1 1 2\n", | |
| " 0 3 0 2 3 2 0 3 3 2 1 3 1 3 1 0 0 1 1 1 3 0 0 1 3 3 1 0 0 3 3 1 2 2 2 0 1\n", | |
| " 2 0 1 3 2 2 3 2 1 0 1 3 1 3 3 0 3 3 2 1 3 2 2 3 1 1 0 0 1 0 1 3 2 0 1 1 0\n", | |
| " 0 3 1 3 2 3 2 0 2 1 3 2 1 3 3 0 2 0 2 3 0 2 2 0 3 1 0 0 2 2 1 2 2 0 0 0 1\n", | |
| " 1 2 3 1 1 0 2 2 0 1 0 2 2 3 3 3 1 0 1 2 2 3 3 0 1 0 3 1 1 2 1 0 0 0 0 0 3\n", | |
| " 2 0 3 0 0 0 0 1 3 3 1 0 1 1 1 1 2 2 3 3 3 1 2 0 0 0 2 1 1 3 1 1 2 1 1 3 2\n", | |
| " 3 0 0 2 1 3 0 1 2 0 2 3 2 0 1 3 3 0 1 3 3 3 0 3 1 2 3 3 2 1 0 3 3 1 3 3 3\n", | |
| " 3 3 0 1 2 2 2 3 0 2 3 2 2 2 1 0 2 0 3 3 1 3 1 1 3 1 2 0 0 3 0 1 2 3 3 3 1\n", | |
| " 1 0 1 3 3 0 1 2 2 0 3 3 2 3 2 3 2 0 2 1 1 1 0 0 0 3 3 3 1 0 1 0 1 2 3 0 3\n", | |
| " 3 2 1 3 0 0 2 1 3 2 0 1 1 1 1 1 3 2 0 0 3 3 0 3 0 0 2 0 1 2 2 2 3 0 3 2 3\n", | |
| " 3 3 3 2 1 1 0 3 1 3 3 0 2 3 2 3 3 3 0 0 2 3 0 0 2 3 2 1 1 2 1 3 0 3 1 2 0\n", | |
| " 0 1 0 1 0 1 0 2 2 3 2 1 1 2 1 1 3 1 0 0 3 0 1 0 0 2 3 3 0 2 0 1 1 3 3 1 2\n", | |
| " 0 2 0 0 3 3 0 2 2 2 3 1 2 0 1 3 1 0 3 1 0 0 3 2 3 2 0 2 1 0 1 2 3 2 1 1 0\n", | |
| " 1 2 2 1 0 1 3 1 2 0 2 2 3 0 0 1 2 3 1]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m3=m3.predict(x_test)\n", | |
| "print(ypred_m3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 171, | |
| "id": "05b042e0", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix,classification_report" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 172, | |
| "id": "d8b9303d", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[127 5 0 0]\n", | |
| " [ 1 117 0 0]\n", | |
| " [ 0 3 112 5]\n", | |
| " [ 0 0 1 129]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.99 0.96 0.98 132\n", | |
| " 1 0.94 0.99 0.96 118\n", | |
| " 2 0.99 0.93 0.96 120\n", | |
| " 3 0.96 0.99 0.98 130\n", | |
| "\n", | |
| " accuracy 0.97 500\n", | |
| " macro avg 0.97 0.97 0.97 500\n", | |
| "weighted avg 0.97 0.97 0.97 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(confusion_matrix(y_test,ypred_m3))\n", | |
| "print(classification_report(y_test,ypred_m3))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 173, | |
| "id": "1ea71973", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.97\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('train score',m3.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "278a84c7", | |
| "metadata": {}, | |
| "source": [ | |
| "# (d)Decision Tree Classifier\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 174, | |
| "id": "961480bf", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "#(d)Decision Tree Classifier\n", | |
| "from sklearn.tree import DecisionTreeClassifier\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 175, | |
| "id": "ddb57ea4", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "DecisionTreeClassifier(criterion='entropy', max_depth=5)" | |
| ] | |
| }, | |
| "execution_count": 175, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "m4 = DecisionTreeClassifier(criterion='entropy',max_depth=5)\n", | |
| "m4.fit(x_train,y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 176, | |
| "id": "f09f8a74", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "training score 0.8793333333333333\n", | |
| "testing score 0.826\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "nscore(m4)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 177, | |
| "id": "5f52e8f9", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[121 11 0 0]\n", | |
| " [ 11 97 10 0]\n", | |
| " [ 0 19 82 19]\n", | |
| " [ 0 0 17 113]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.92 0.92 0.92 132\n", | |
| " 1 0.76 0.82 0.79 118\n", | |
| " 2 0.75 0.68 0.72 120\n", | |
| " 3 0.86 0.87 0.86 130\n", | |
| "\n", | |
| " accuracy 0.83 500\n", | |
| " macro avg 0.82 0.82 0.82 500\n", | |
| "weighted avg 0.83 0.83 0.83 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m4=m4.predict(x_test)\n", | |
| "eval_model(y_test,ypred_m4)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 178, | |
| "id": "3b2cea88", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.8793333333333333\n", | |
| "train score 0.826\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Accuracy\n", | |
| "print('train score',m4.score(x_train,y_train))\n", | |
| "print('train score',m4.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 179, | |
| "id": "e6ba891e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[0 1 1 3 1 1 2 0 2 1 0 1 2 3 2 2 3 3 1 0 0 2 1 2 0 1 2 2 2 0 0 0 3 0 1 1 3\n", | |
| " 0 3 0 1 3 2 0 2 2 2 1 3 1 3 1 0 0 1 1 1 2 0 0 0 3 3 1 0 0 3 3 1 2 1 2 0 1\n", | |
| " 3 0 1 3 2 1 3 2 1 0 2 3 2 3 3 0 2 3 1 1 3 2 2 3 1 1 0 0 0 0 0 3 2 0 1 1 0\n", | |
| " 0 2 1 2 2 2 3 0 2 1 3 1 1 3 3 0 3 0 2 3 0 2 2 0 2 1 0 0 2 3 1 3 3 0 0 0 1\n", | |
| " 2 3 3 2 0 0 2 2 0 2 0 1 2 3 2 3 1 0 0 2 2 3 3 1 1 0 3 1 1 2 1 0 0 0 0 0 3\n", | |
| " 2 0 3 0 0 0 0 1 3 2 2 0 0 1 1 1 2 2 2 3 3 1 2 0 0 0 2 1 1 3 1 1 2 1 1 3 2\n", | |
| " 3 0 0 1 1 3 0 0 1 0 2 3 2 1 1 3 3 0 1 3 3 3 0 3 1 2 3 3 2 1 1 3 3 1 3 3 3\n", | |
| " 3 3 0 1 2 3 1 3 0 1 3 2 2 2 1 0 1 0 2 3 1 3 1 0 3 1 2 0 0 3 0 1 2 3 3 3 1\n", | |
| " 1 0 1 3 3 0 2 2 2 0 3 3 2 3 2 3 2 0 2 1 1 1 0 0 0 3 2 3 2 0 1 0 2 3 3 1 2\n", | |
| " 3 2 1 3 0 0 3 1 3 2 0 1 1 1 0 1 3 1 0 0 3 3 0 3 0 0 2 0 1 2 2 2 3 0 3 2 2\n", | |
| " 3 3 3 2 1 1 0 3 1 3 3 0 2 3 1 3 3 3 0 0 2 3 0 0 2 3 1 1 1 2 2 3 1 3 2 2 0\n", | |
| " 1 1 0 1 0 1 0 2 2 3 2 1 1 3 1 1 3 1 0 0 3 0 1 0 0 1 3 3 0 2 0 1 1 3 3 0 3\n", | |
| " 1 2 0 0 3 3 0 1 2 2 3 1 2 0 1 3 1 0 3 2 0 0 3 2 3 2 0 3 1 0 1 2 3 2 1 0 0\n", | |
| " 1 2 1 1 1 1 3 1 2 0 3 3 3 0 0 0 2 3 1]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m4=m4.predict(x_test)\n", | |
| "print(ypred_m4)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 180, | |
| "id": "ff999dc0", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix,classification_report" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 181, | |
| "id": "d37a464e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[121 11 0 0]\n", | |
| " [ 11 97 10 0]\n", | |
| " [ 0 19 82 19]\n", | |
| " [ 0 0 17 113]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.92 0.92 0.92 132\n", | |
| " 1 0.76 0.82 0.79 118\n", | |
| " 2 0.75 0.68 0.72 120\n", | |
| " 3 0.86 0.87 0.86 130\n", | |
| "\n", | |
| " accuracy 0.83 500\n", | |
| " macro avg 0.82 0.82 0.82 500\n", | |
| "weighted avg 0.83 0.83 0.83 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(confusion_matrix(y_test,ypred_m4))\n", | |
| "print(classification_report(y_test,ypred_m4))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 182, | |
| "id": "0a1eb1ab", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.826\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('train score',m4.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 183, | |
| "id": "d392f771", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Index(['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',\n", | |
| " 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',\n", | |
| " 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',\n", | |
| " 'touch_screen', 'wifi'],\n", | |
| " dtype='object')\n", | |
| "['0', '1']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "fn = x_train.columns\n", | |
| "cn = ['0','1']\n", | |
| "print(fn)\n", | |
| "print(cn)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "3c95769b", | |
| "metadata": {}, | |
| "source": [ | |
| "# (e)Random forest Classifier\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 184, | |
| "id": "5118604b", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.ensemble import RandomForestClassifier" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 185, | |
| "id": "ba453d34", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "RandomForestClassifier(criterion='entropy', max_depth=7, n_estimators=80)" | |
| ] | |
| }, | |
| "execution_count": 185, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "m5=RandomForestClassifier(n_estimators=80,criterion='entropy',max_depth=7)\n", | |
| "m5.fit(x_train,y_train)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 186, | |
| "id": "fe2aee8d", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "training score 0.9713333333333334\n", | |
| "testing score 0.874\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "nscore(m5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 187, | |
| "id": "82f28d7b", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[124 8 0 0]\n", | |
| " [ 10 97 11 0]\n", | |
| " [ 0 14 89 17]\n", | |
| " [ 0 0 3 127]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.93 0.94 0.93 132\n", | |
| " 1 0.82 0.82 0.82 118\n", | |
| " 2 0.86 0.74 0.80 120\n", | |
| " 3 0.88 0.98 0.93 130\n", | |
| "\n", | |
| " accuracy 0.87 500\n", | |
| " macro avg 0.87 0.87 0.87 500\n", | |
| "weighted avg 0.87 0.87 0.87 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m5=m5.predict(x_test)\n", | |
| "eval_model(y_test,ypred_m5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 188, | |
| "id": "a299dc61", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.9713333333333334\n", | |
| "train score 0.874\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Accuracy\n", | |
| "print('train score',m5.score(x_train,y_train))\n", | |
| "print('train score',m5.score(x_test,y_test))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 189, | |
| "id": "a021aa67", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[0 2 1 3 1 2 2 0 3 1 0 1 2 3 3 2 3 3 1 0 0 1 1 2 0 1 3 2 2 0 0 0 3 0 1 1 3\n", | |
| " 0 3 0 2 3 2 0 3 3 2 1 3 1 3 1 0 0 1 0 1 3 0 0 0 3 3 1 0 0 3 3 1 2 2 2 0 1\n", | |
| " 3 0 0 3 2 2 3 2 1 0 1 3 2 3 3 0 3 3 2 1 3 2 2 3 2 1 0 0 1 0 0 3 2 0 1 1 0\n", | |
| " 0 3 1 3 2 3 3 0 2 1 3 3 1 3 3 0 3 1 2 3 0 2 2 0 3 1 0 0 2 3 0 2 3 0 0 0 1\n", | |
| " 2 2 3 1 1 0 2 2 0 1 0 2 2 3 3 3 1 0 0 2 2 3 3 1 1 0 3 1 1 2 1 0 0 0 0 0 3\n", | |
| " 2 0 3 0 0 0 0 1 3 3 1 0 1 2 1 1 2 2 3 3 3 1 2 0 0 0 2 1 1 3 1 0 2 2 1 3 1\n", | |
| " 3 0 0 2 1 3 0 0 1 0 1 3 2 0 1 3 3 0 1 3 3 3 0 3 1 2 3 3 3 1 1 3 3 1 3 3 3\n", | |
| " 3 3 0 1 2 2 2 3 0 2 3 2 3 2 1 0 2 0 3 3 1 3 1 0 3 1 2 0 0 3 0 1 2 3 3 3 1\n", | |
| " 1 0 1 3 3 0 1 1 2 0 3 3 2 3 1 3 2 0 2 1 2 1 0 0 0 3 3 3 1 0 1 1 2 2 2 0 3\n", | |
| " 3 2 1 3 0 0 3 1 3 2 0 1 1 2 1 1 3 1 0 0 3 3 0 3 0 0 1 0 0 2 2 2 3 0 3 2 2\n", | |
| " 3 3 3 2 1 2 0 3 2 3 3 0 2 3 2 3 3 3 0 0 2 3 0 0 2 3 1 1 1 2 1 2 0 3 1 2 0\n", | |
| " 0 1 0 1 0 2 1 2 2 3 2 1 1 3 1 0 3 1 0 0 3 0 1 0 0 1 3 3 0 2 1 1 1 3 3 0 2\n", | |
| " 0 2 0 0 3 3 0 2 2 1 3 1 1 0 1 3 1 0 3 1 0 0 3 2 3 2 0 2 0 0 1 2 3 2 1 1 0\n", | |
| " 1 2 2 1 1 1 3 1 2 0 3 3 3 0 0 1 2 3 1]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "ypred_m5=m5.predict(x_test)\n", | |
| "print(ypred_m5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 190, | |
| "id": "c277f0cc", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.metrics import confusion_matrix,classification_report" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 191, | |
| "id": "5b18949e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[124 8 0 0]\n", | |
| " [ 10 97 11 0]\n", | |
| " [ 0 14 89 17]\n", | |
| " [ 0 0 3 127]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.93 0.94 0.93 132\n", | |
| " 1 0.82 0.82 0.82 118\n", | |
| " 2 0.86 0.74 0.80 120\n", | |
| " 3 0.88 0.98 0.93 130\n", | |
| "\n", | |
| " accuracy 0.87 500\n", | |
| " macro avg 0.87 0.87 0.87 500\n", | |
| "weighted avg 0.87 0.87 0.87 500\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(confusion_matrix(y_test,ypred_m5))\n", | |
| "print(classification_report(y_test,ypred_m5))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 192, | |
| "id": "7825a333", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "train score 0.874\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('train score',m5.score(x_test,y_test))" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "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": "3.10.7 (tags/v3.10.7:6cc6b13, Sep 5 2022, 14:08:36) [MSC v.1933 64 bit (AMD64)]" | |
| }, | |
| "vscode": { | |
| "interpreter": { | |
| "hash": "22a7a93b3c39d93f69fcd4b86203bbf6ef8378a8960b182d8683d26141f52fbf" | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } | |