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‎basics/AverageMarks.xlsx

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‎basics/Dummies & OHE.ipynb

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‎basics/Student_ List.xlsx

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‎basics/Untitled.ipynb

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‎basics/Untitled1.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "18373d59",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from sklearn import svm, datasets\n",
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"iris = datasets.load_iris()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7149cb62",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>sepal length (cm)</th>\n",
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" <th>sepal width (cm)</th>\n",
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" <th>petal length (cm)</th>\n",
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" <th>petal width (cm)</th>\n",
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" <th>flower</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>47</th>\n",
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" <td>4.6</td>\n",
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" <td>3.2</td>\n",
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" <td>1.4</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>48</th>\n",
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" <td>5.3</td>\n",
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" <td>3.7</td>\n",
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" <td>1.5</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>49</th>\n",
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" <td>5.0</td>\n",
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" <td>3.3</td>\n",
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" <td>1.4</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50</th>\n",
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" <td>7.0</td>\n",
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" <td>3.2</td>\n",
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" <td>4.7</td>\n",
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" <td>1.4</td>\n",
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" <td>versicolor</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>51</th>\n",
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" <td>6.4</td>\n",
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" <td>3.2</td>\n",
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" <td>4.5</td>\n",
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" <td>1.5</td>\n",
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" <td>versicolor</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>145</th>\n",
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" <td>6.7</td>\n",
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" <td>3.0</td>\n",
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" <td>5.2</td>\n",
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" <td>2.3</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>146</th>\n",
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" <td>6.3</td>\n",
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" <td>2.5</td>\n",
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" <td>5.0</td>\n",
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" <td>1.9</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>147</th>\n",
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" <td>6.5</td>\n",
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" <td>3.0</td>\n",
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" <td>5.2</td>\n",
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" <td>2.0</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>148</th>\n",
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" <td>6.2</td>\n",
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" <td>3.4</td>\n",
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" <td>5.4</td>\n",
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" <td>2.3</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>149</th>\n",
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" <td>5.9</td>\n",
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" <td>3.0</td>\n",
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" <td>5.1</td>\n",
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" <td>1.8</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>103 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
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"47 4.6 3.2 1.4 0.2 \n",
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"48 5.3 3.7 1.5 0.2 \n",
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"49 5.0 3.3 1.4 0.2 \n",
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"50 7.0 3.2 4.7 1.4 \n",
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"51 6.4 3.2 4.5 1.5 \n",
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".. ... ... ... ... \n",
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"145 6.7 3.0 5.2 2.3 \n",
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"146 6.3 2.5 5.0 1.9 \n",
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"147 6.5 3.0 5.2 2.0 \n",
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"148 6.2 3.4 5.4 2.3 \n",
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"149 5.9 3.0 5.1 1.8 \n",
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"\n",
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" flower \n",
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"47 setosa \n",
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"48 setosa \n",
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"49 setosa \n",
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"50 versicolor \n",
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"51 versicolor \n",
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".. ... \n",
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"145 virginica \n",
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"146 virginica \n",
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"147 virginica \n",
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"148 virginica \n",
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"149 virginica \n",
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"\n",
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"[103 rows x 5 columns]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
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"df['flower'] = iris.target\n",
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"df['flower'] = df['flower'].apply(lambda x: iris.target_names[x])\n",
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"df[47:150]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "01221468",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "85bd2f18",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.9777777777777777"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"model = svm.SVC(kernel='rbf',C=30,gamma='auto')\n",
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"model.fit(X_train,y_train)\n",
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"model.score(X_test, y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "0d7f7aeb",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'cross_val_score' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-5-044c4818af04>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'linear'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m: name 'cross_val_score' is not defined"
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]
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}
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],
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"source": [
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"cross_val_score(svm.SVC(kernel='linear',C=10,gamma='auto'),iris.data, iris.target, cv=5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "67b2e343",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'cross_val_score' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-6-2ee90564f888>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m: name 'cross_val_score' is not defined"
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]
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}
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],
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"source": [
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"cross_val_score(svm.SVC(kernel='rbf',C=10,gamma='auto'),iris.data, iris.target, cv=5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "26102575",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎basics/alloted.xlsx

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‎basics/areas.csv

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areas,prices
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2000,13438.356164383564
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4500,62582.19178082192
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4000,52753.42465753425
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3100,35061.643835616436
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4650,65530.821917808214

‎basics/carprice.csv

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Car Model,Mileage,Sell Price($),Age(yrs)
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BMW X5,69000,18000,6
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BMW X5,35000,34000,3
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BMW X5,57000,26100,5
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BMW X5,22500,40000,2
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BMW X5,46000,31500,4
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Audi A5,59000,29400,5
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Audi A5,52000,32000,5
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Audi A5,72000,19300,6
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Audi A5,91000,12000,8
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Mercedez Benz C class,67000,22000,6
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Mercedez Benz C class,83000,20000,7
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Mercedez Benz C class,79000,21000,7
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Mercedez Benz C class,59000,33000,5

‎basics/carprices.csv

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Mileage,Age(yrs),Sell Price($)
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69000,6,18000
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35000,3,34000
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57000,5,26100
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22500,2,40000
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46000,4,31500
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59000,5,26750
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52000,5,32000
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72000,6,19300
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91000,8,12000
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67000,6,22000
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83000,7,18700
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79000,7,19500
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59000,5,26000
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58780,4,27500
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82450,7,19400
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25400,3,35000
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28000,2,35500
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69000,5,19700
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87600,8,12800
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52000,5,28200

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