├── images ├── dummy.txt ├── fb.png ├── ig.png ├── tw.png ├── add.png ├── arrow.png ├── logo.png ├── menu.png └── controller.png ├── index.html ├── Dictionary_Pratice.ipynb ├── style.css ├── Feature_Engineering_practice.ipynb ├── website.html ├── Practice_data_pre.ipynb ├── webstyle.css ├── class_pratice.ipynb ├── Practice_for_Day_6.ipynb ├── Cross_Validation_Practice.ipynb ├── Logistice_Regression.ipynb ├── Linear_Rgression_Basics_Prg_.ipynb └── Model_Wizard.ipynb /images/dummy.txt: -------------------------------------------------------------------------------- 1 | Hello 2 | -------------------------------------------------------------------------------- /images/fb.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/fb.png -------------------------------------------------------------------------------- /images/ig.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/ig.png -------------------------------------------------------------------------------- /images/tw.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/tw.png -------------------------------------------------------------------------------- /images/add.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/add.png -------------------------------------------------------------------------------- /images/arrow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/arrow.png -------------------------------------------------------------------------------- /images/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/logo.png -------------------------------------------------------------------------------- /images/menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/menu.png -------------------------------------------------------------------------------- /images/controller.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RohithRagavender/practice/HEAD/images/controller.png -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Document 7 | 8 | 9 | 10 |

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PS4 V2
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Wireless Controller for Playstaion 4

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Add To Cart

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48 | 49 | 54 | 70 | 71 | 72 | -------------------------------------------------------------------------------- /Dictionary_Pratice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyNXYBJU6ceTZ2MoFF3PHeqy", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 8, 32 | "metadata": { 33 | "colab": { 34 | "base_uri": "https://localhost:8080/" 35 | }, 36 | "id": "XyZ9CHY-WfNU", 37 | "outputId": "837983ac-9445-4729-c7a9-d6c257c63b03" 38 | }, 39 | "outputs": [ 40 | { 41 | "output_type": "stream", 42 | "name": "stdout", 43 | "text": [ 44 | "rog --> vowels: o\n", 45 | "max --> vowels: a\n", 46 | "love --> vowels: o\n", 47 | "love --> vowels: e\n", 48 | "computer --> vowels: o\n", 49 | "computer --> vowels: u\n", 50 | "computer --> vowels: e\n" 51 | ] 52 | } 53 | ], 54 | "source": [ 55 | "a=['rog','max','love','gym','computer']\n", 56 | "b=['a','e','i','o','u']\n", 57 | "\n", 58 | "for i in a:\n", 59 | " for j in i:\n", 60 | " if j in b:\n", 61 | " print(i,\"-->\",\"vowels:\",j)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "source": [ 67 | "words=\"Presenting the lyrical video of the song Chammak Chhalo from the Bollywood movie Ra.One.The movie features Shahrukh Khan, Kareena Kapoor, Arjun Rampal, Armaan Verma, Shahana Goswami, Tom Wu in leading roles,Just keep giving us your love and keep listening to T-Series. Enjoy & stay connected with us \"\n", 68 | "dic={'a':0,'e':0,'i':0,'o':0,'u':0}\n", 69 | "\n", 70 | "for i in words.lower():\n", 71 | " if i in dic:\n", 72 | " dic[i]+=1\n", 73 | "\n", 74 | "for v,count in enumerate(dic.items()):\n", 75 | " print(count)\n" 76 | ], 77 | "metadata": { 78 | "colab": { 79 | "base_uri": "https://localhost:8080/" 80 | }, 81 | "id": "M7_Il20qXzpa", 82 | "outputId": "60b004cf-cc70-4952-8744-46d2a6756469" 83 | }, 84 | "execution_count": 22, 85 | "outputs": [ 86 | { 87 | "output_type": "stream", 88 | "name": "stdout", 89 | "text": [ 90 | "('a', 25)\n", 91 | "('e', 28)\n", 92 | "('i', 14)\n", 93 | "('o', 21)\n", 94 | "('u', 8)\n" 95 | ] 96 | } 97 | ] 98 | } 99 | ] 100 | } -------------------------------------------------------------------------------- /style.css: -------------------------------------------------------------------------------- 1 | *{ 2 | margin: 0; 3 | font-family: sans-serif; 4 | padding: 0; 5 | 6 | } 7 | 8 | .logo{ 9 | width: 50px; 10 | margin: 30px 0; 11 | cursor: pointer; 12 | } 13 | 14 | .menu-icon{ 15 | width: 25px; 16 | cursor: pointer; 17 | display: none; 18 | } 19 | nav{ 20 | flex: 1; 21 | text-align: right; 22 | 23 | } 24 | nav ul li{ 25 | list-style: none; 26 | display: inline-block; 27 | margin: 30px ; 28 | } 29 | nav ul li a { 30 | text-decoration: none; 31 | color: #000; 32 | font-size: 14px ; 33 | } 34 | 35 | nav ul li a:hover{ 36 | color: #ff5ea2; 37 | } 38 | .container{ 39 | width: 100%; 40 | min-height: 100%; 41 | box-sizing: border-box; 42 | padding-left: 8%; 43 | padding-right: 8%; 44 | overflow: hidden; 45 | 46 | } 47 | .navbar 48 | { 49 | width: 100%; 50 | display: flex; 51 | align-items: center; 52 | } 53 | 54 | 55 | .row{ 56 | display: flex; 57 | justify-content: space-between; 58 | align-items: center; 59 | margin: 100px 0; 60 | } 61 | 62 | .col-1{ 63 | flex-basis:40%; 64 | position: relative; 65 | margin-left: 50px; 66 | } 67 | 68 | .col-1 h2{ 69 | font-size: 54px; 70 | 71 | } 72 | .col-1 h3{ 73 | font-size: 30px; 74 | color: #707070; 75 | font-weight: 100; 76 | margin: 20px 0 10px; 77 | } 78 | 79 | .col-1 p{ 80 | font-size: 16px; 81 | color: #b7b7b7; 82 | font-weight: 100; 83 | } 84 | 85 | .col-1 h4{ 86 | margin: 30px; 87 | font-size: 20px 88 | 89 | } 90 | 91 | button{ 92 | width: 100px; 93 | border: 0; 94 | padding: 12px 10px; 95 | outline: none; 96 | background:linear-gradient(to right,#fb5283,#ff3527); 97 | border-radius: 6px; 98 | cursor: pointer; 99 | transition: 1s; 100 | 101 | } 102 | button img{ 103 | width: 30px; 104 | display:none; 105 | } 106 | button :hover img { 107 | display: block; 108 | 109 | } 110 | 111 | button:hover{ 112 | transition: 1s; 113 | width: 160px; 114 | border-radius: 24px; 115 | } 116 | .col-1::after{ 117 | content: ''; 118 | width: 10px; 119 | height: 57%; 120 | background:linear-gradient(#ff469f,#ff6062); 121 | position: absolute; 122 | left: -40px; 123 | top: 8px; 124 | } 125 | 126 | .col-2{ 127 | position: relative; 128 | flex-basis:60% ; 129 | display: flex; 130 | align-items: center; 131 | } 132 | 133 | .col-2.controller{ 134 | width: 90%; 135 | } 136 | 137 | .color-box{ 138 | position: absolute; 139 | right: 0; 140 | top: 0; 141 | background: linear-gradient(#ff54a2,#ff575a); 142 | border-radius: 20px 0 0 20px; 143 | height: 100%; 144 | width: 80%; 145 | z-index: -1; 146 | transform: translateX(150px); 147 | } 148 | 149 | .add-btn img{ 150 | width: 35px; 151 | margin-bottom: 5px ; 152 | 153 | } 154 | 155 | .add-btn{ 156 | 157 | text-align: center; 158 | color: white; 159 | cursor: pointer; 160 | } 161 | 162 | .social-link img{ 163 | height: 13px; 164 | margin: 20px; 165 | cursor: pointer; 166 | } 167 | 168 | .social-link{ 169 | text-align: center; 170 | } 171 | 172 | @media only screen and (max-width:700px) 173 | { 174 | 175 | 176 | nav ul { 177 | width: 100%; 178 | background: linear-gradient(#ff54a2,#ff575a); 179 | position: absolute; 180 | top: 75px; 181 | right: 0; 182 | z-index: 2; 183 | } 184 | nav ul li{ 185 | display: block; 186 | margin-top: 10px; 187 | margin-bottom: 10px; 188 | 189 | } 190 | nav ul li a { 191 | color: #fff; 192 | } 193 | } 194 | 195 | .menu-icon{ 196 | display: block; 197 | } 198 | 199 | #menuList{ 200 | overflow: hidden; 201 | transition: 0.5s; 202 | } 203 | .row{ 204 | flex-direction: column-reverse; 205 | margin: 50px 0; 206 | } 207 | 208 | .col-2{ 209 | flex-basis: 100%; 210 | margin-bottom: 50px; 211 | } 212 | .col-2.controller{ 213 | width: 77%; 214 | 215 | } 216 | .color-box{ 217 | transform: translateX(75px); 218 | 219 | } 220 | .col-1{ 221 | flex-basis: 100%; 222 | 223 | } 224 | .col-1 h2{ 225 | font-size: 34px; 226 | } 227 | .col-1 h3{ 228 | font-size: 15px; 229 | } -------------------------------------------------------------------------------- /Feature_Engineering_practice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyNWAbnlmISsSbzWXOdYEDKN", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 21, 32 | "metadata": { 33 | "colab": { 34 | "base_uri": "https://localhost:8080/" 35 | }, 36 | "id": "uMAvhb0tF63K", 37 | "outputId": "52f7ba4b-0b24-4428-d15d-1b8d863ec2ec" 38 | }, 39 | "outputs": [ 40 | { 41 | "output_type": "stream", 42 | "name": "stdout", 43 | "text": [ 44 | " Product Category Price Discount Purchase_Date\n", 45 | "0 Shirt Clothing 1200 10 2024-01-10\n", 46 | "1 Shoes Footwear 2500 20 2024-01-15\n", 47 | "2 Watch Accessories 5000 15 2024-02-20\n", 48 | "3 Shoes Footwear 3000 10 2024-02-25\n", 49 | "4 Shirt Clothing 1500 5 2024-03-05\n", 50 | "5 Watch Accessories 4500 25 2024-03-10\n", 51 | "\n", 52 | "\n", 53 | " Product Price Discount Category_Accessories Category_Clothing \\\n", 54 | "0 0 -1.238728 -0.620174 False True \n", 55 | "1 1 -0.318530 0.868243 False False \n", 56 | "2 2 1.451081 0.124035 True False \n", 57 | "3 1 0.035392 -0.620174 False False \n", 58 | "4 0 -1.026375 -1.364382 False True \n", 59 | "5 2 1.097159 1.612452 True False \n", 60 | "\n", 61 | " Category_Footwear Year Month Day \n", 62 | "0 False 2024 1 10 \n", 63 | "1 True 2024 1 15 \n", 64 | "2 False 2024 2 20 \n", 65 | "3 True 2024 2 25 \n", 66 | "4 False 2024 3 5 \n", 67 | "5 False 2024 3 10 \n" 68 | ] 69 | } 70 | ], 71 | "source": [ 72 | "import pandas as pd\n", 73 | "\n", 74 | "data = {\n", 75 | " 'Product': ['Shirt', 'Shoes', 'Watch', 'Shoes', 'Shirt', 'Watch'],\n", 76 | " 'Category': ['Clothing', 'Footwear', 'Accessories', 'Footwear', 'Clothing', 'Accessories'],\n", 77 | " 'Price': [1200, 2500, 5000, 3000, 1500, 4500],\n", 78 | " 'Discount': [10, 20, 15, 10, 5, 25],\n", 79 | " 'Purchase_Date': ['2024-01-10', '2024-01-15', '2024-02-20', '2024-02-25', '2024-03-05', '2024-03-10']\n", 80 | "}\n", 81 | "\n", 82 | "df = pd.DataFrame(data)\n", 83 | "print(df)\n", 84 | "\n", 85 | "\n", 86 | "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n", 87 | "\n", 88 | "print(\"\\n\")\n", 89 | "le = LabelEncoder()\n", 90 | "df['Product']=le.fit_transform(df['Product'])\n", 91 | "#print(df)\n", 92 | "# grp=df.groupby('Category')\n", 93 | "# print('\\n')\n", 94 | "# print(grp.sum())\n", 95 | "\n", 96 | "df=pd.get_dummies(df,columns=['Category'])\n", 97 | "ss=StandardScaler()\n", 98 | "df[['Price','Discount']]=ss.fit_transform(df[['Price','Discount']])\n", 99 | "\n", 100 | "df['Purchase_Date']=pd.to_datetime(df['Purchase_Date'])\n", 101 | "df['Year']=df['Purchase_Date'].dt.year\n", 102 | "df['Month']=df['Purchase_Date'].dt.month\n", 103 | "df['Day']=df['Purchase_Date'].dt.day\n", 104 | "df.drop('Purchase_Date',axis=1,inplace=True)\n", 105 | "print(df)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "markdown", 110 | "source": [ 111 | "📝 Your Tasks\n", 112 | "Label Encode the \"Product\" column.\n", 113 | "\n", 114 | "One-Hot Encode the \"Category\" column.\n", 115 | "\n", 116 | "Apply StandardScaler on \"Price\" and \"Discount\".\n", 117 | "\n", 118 | "Create New Features:\n", 119 | "\n", 120 | "Year, Month, Day from \"Purchase_Date\".\n", 121 | "\n", 122 | "Drop the \"Purchase_Date\" column after feature extraction." 123 | ], 124 | "metadata": { 125 | "id": "sYgsPd1YGV5U" 126 | } 127 | } 128 | ] 129 | } -------------------------------------------------------------------------------- /website.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | BootCamp 7 | 8 | 9 | 10 | 11 |

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115 | 116 | -------------------------------------------------------------------------------- /Practice_data_pre.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyPX2IZUbDMRv5TX3F23s0ef", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 24, 32 | "metadata": { 33 | "colab": { 34 | "base_uri": "https://localhost:8080/" 35 | }, 36 | "id": "AJtAaQc5DTO-", 37 | "outputId": "597ede4e-6fa2-4185-b53c-5b2cd27dcccf" 38 | }, 39 | "outputs": [ 40 | { 41 | "output_type": "stream", 42 | "name": "stdout", 43 | "text": [ 44 | "Missing Values Count Before Handling\n", 45 | "EmpID 0\n", 46 | "Name 0\n", 47 | "Department 0\n", 48 | "Gender 1\n", 49 | "Age 2\n", 50 | "Experience 0\n", 51 | "Salary 1\n", 52 | "JoiningCity 0\n", 53 | "Rating 0\n", 54 | "dtype: int64\n", 55 | "\n", 56 | "\n", 57 | "\n", 58 | "\n", 59 | "Mean Value\n", 60 | "Age -2.220446e-17\n", 61 | "Salary 2.886580e-16\n", 62 | "Experience 9.992007e-17\n", 63 | "dtype: float64\n", 64 | "\n", 65 | "\n", 66 | "Standard Deivation Value\n", 67 | "Age 1.054093\n", 68 | "Salary 1.054093\n", 69 | "Experience 1.054093\n", 70 | "dtype: float64\n" 71 | ] 72 | }, 73 | { 74 | "output_type": "stream", 75 | "name": "stderr", 76 | "text": [ 77 | "/tmp/ipython-input-3519740358.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 78 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 79 | "\n", 80 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 81 | "\n", 82 | "\n", 83 | " df['Age'].fillna(df['Age'].median(),inplace=True)\n", 84 | "/tmp/ipython-input-3519740358.py:7: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 85 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 86 | "\n", 87 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 88 | "\n", 89 | "\n", 90 | " df['Salary'].fillna(df['Salary'].mean(),inplace=True)\n", 91 | "/tmp/ipython-input-3519740358.py:8: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 92 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 93 | "\n", 94 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 95 | "\n", 96 | "\n", 97 | " df['Gender'].fillna(\"Unknown\",inplace=True)\n" 98 | ] 99 | } 100 | ], 101 | "source": [ 102 | "import pandas as pd\n", 103 | "from sklearn.preprocessing import LabelEncoder,StandardScaler\n", 104 | "df=pd.read_csv('employee_data.csv')\n", 105 | "print(\"Missing Values Count Before Handling\")\n", 106 | "print(df.isnull().sum())\n", 107 | "df['Age'].fillna(df['Age'].median(),inplace=True)\n", 108 | "df['Salary'].fillna(df['Salary'].mean(),inplace=True)\n", 109 | "df['Gender'].fillna(\"Unknown\",inplace=True)\n", 110 | "print(\"\\n\")\n", 111 | "# Starting to Encoding Section\n", 112 | "le=LabelEncoder()\n", 113 | "df['Gender']=le.fit_transform(df['Gender'])\n", 114 | "df['Department'] = le.fit_transform(df['Department'])\n", 115 | "df = pd.get_dummies(df,columns=['JoiningCity'],drop_first=True)\n", 116 | "print(\"\\n\")\n", 117 | "# Starting to Scaling the Data\n", 118 | "scaler=StandardScaler()\n", 119 | "cols_to_scale=['Age','Salary','Experience']\n", 120 | "df[cols_to_scale]=scaler.fit_transform(df[cols_to_scale])\n", 121 | "print(\"Mean Value\")\n", 122 | "print(df[cols_to_scale].mean())\n", 123 | "print(\"\\n\")\n", 124 | "print(\"Standard Deivation Value\")\n", 125 | "print(df[cols_to_scale].std())" 126 | ] 127 | } 128 | ] 129 | } -------------------------------------------------------------------------------- /webstyle.css: -------------------------------------------------------------------------------- 1 | *{ 2 | margin:0; 3 | padding:0; 4 | } 5 | .BootCamp{ 6 | padding-left: 8%; 7 | font-family: 'Lucida Sans', 'Lucida Sans Regular', 'Lucida Grande', 'Lucida Sans Unicode', Geneva, Verdana, sans-serif; 8 | color: white; 9 | float: left; 10 | } 11 | 12 | .middle{ 13 | width: 100%; 14 | } 15 | .front 16 | { 17 | width: 100%; 18 | height: 70vh; 19 | background-color:#212529; 20 | padding-top: 20px; 21 | font-size: 20px; 22 | padding-left: 0; 23 | list-style-type: none; 24 | } 25 | 26 | .top{ 27 | width: 90%; 28 | height:20px; 29 | padding: 2rex; 30 | padding-left: 5px; 31 | padding-top: 0; 32 | background-color: #212529; 33 | color: whitesmoke; 34 | } 35 | 36 | ul{ 37 | list-style-type: none; 38 | float:right; 39 | display: flex; 40 | margin-right: 10px; 41 | padding-left: 0; 42 | padding: 0; 43 | 44 | } 45 | 46 | .top a:hover{ 47 | padding-top: 0%; 48 | text-decoration: none; 49 | color:rgba(255,255,255,.55); 50 | transition: 1ms; 51 | } 52 | 53 | .image{ 54 | margin:58px; 55 | width: 38%; 56 | display:inline; 57 | position:absolute; 58 | right: -37px; 59 | padding-right:25%; 60 | padding:15px; 61 | height: 47; 62 | padding-top:63px; 63 | } 64 | 65 | .content{ 66 | padding: 5rem; 67 | padding-right: 20; 68 | color: white; 69 | padding-top: 12%; 70 | padding-left: 8%; 71 | padding-right: 35%; 72 | margin: 25px; 73 | } 74 | 75 | .role{ 76 | color: #0d6efd; 77 | } 78 | 79 | .quote{ 80 | color:rgb(204,204,204); 81 | padding-top: 3%; 82 | margin-top: 10px; 83 | margin-bottom: 10px; 84 | 85 | } 86 | 87 | .button 88 | { width: 20%; 89 | padding-top: 10px; 90 | padding-bottom: 10px; 91 | border-radius: 5px; 92 | border-style: none; 93 | color:rgb(185,185,189); 94 | background-color:#0d6efd; 95 | height: 10%; 96 | font-family:'Franklin Gothic Medium', 'Arial Narrow', Arial, sans-serif; 97 | margin: 10px; 98 | } 99 | .sign { 100 | background-color:#0d6efd; 101 | color: white; 102 | font-family:Arial, Helvetica, sans-serif; 103 | padding: 50px; 104 | } 105 | form{ 106 | display:inline; 107 | cursor:pointer; 108 | } 109 | 110 | .form{ 111 | float:right; 112 | } 113 | 114 | 115 | h2{ 116 | width: 50px; 117 | display: inline; 118 | } 119 | 120 | #email{ 121 | width:600px; 122 | height:50px; 123 | border-radius: 2px; 124 | border-style:none; 125 | font-family:sans-serif; 126 | font-size:20px; 127 | } 128 | .subscribe{ 129 | width:120px; 130 | height:50px; 131 | display:inline; 132 | font-family:sans-serif; 133 | font-size:20px; 134 | background-color:black; 135 | color:white; 136 | } 137 | 138 | .contents{ 139 | margin:50px; 140 | } 141 | 142 | .content1{ 143 | background-color: black; 144 | color: white; 145 | width: 202px; 146 | height: 65%; 147 | padding: 65px; 148 | margin: 50px; 149 | border-radius: 5px ; 150 | font-family: sans-serif; 151 | display: inline-block ; 152 | } 153 | 154 | .content1 h3{ 155 | text-align: center; 156 | font-size: 32px; 157 | margin-right:39px; 158 | } 159 | 160 | .content1 p{ 161 | padding: 2px; 162 | display: inline; 163 | text-align:justify; 164 | } 165 | 166 | .lap{ 167 | background-color:white; 168 | padding: 25px; 169 | margin: 15px; 170 | padding-right:5px; 171 | padding-left:5px; 172 | padding-top:5px; 173 | padding-bottom:5px; 174 | width: 56px; 175 | display: flex; 176 | margin-left: 57px; 177 | } 178 | 179 | .content2{ 180 | display:inline-block; 181 | background-color: black; 182 | color: white; 183 | width: 202px; 184 | height: 65%; 185 | padding: 65px; 186 | margin: 50px; 187 | border-radius: 5px ; 188 | font-family: sans-serif; 189 | 190 | } 191 | 192 | .content2 h3{ 193 | text-align: center; 194 | font-size: 32px; 195 | margin-right:39px; 196 | } 197 | 198 | .content2 p{ 199 | padding: 2px; 200 | display: inline; 201 | text-align:justify; 202 | } 203 | 204 | .content3{ 205 | display:inline-block; 206 | background-color: black; 207 | color: white; 208 | width: 212px; 209 | height: 65%; 210 | padding: 65px; 211 | margin: 50px; 212 | border-radius: 5px ; 213 | font-family: sans-serif; 214 | 215 | } 216 | 217 | .content3 h3{ 218 | text-align: center; 219 | font-size: 32px; 220 | margin-right:39px; 221 | } 222 | 223 | .content3 p{ 224 | padding: 2px; 225 | display: inline; 226 | text-align:justify; 227 | } 228 | 229 | .onclick{ 230 | padding-top: 10px; 231 | margin: 38px; 232 | width: 80px; 233 | display: inline; 234 | background-color: #0d6efd; 235 | color: aliceblue; 236 | font-family: 'Courier New', Courier, monospace; 237 | } 238 | 239 | .java{ 240 | width: 43%; 241 | margin: 0%; 242 | } 243 | 244 | .about{ 245 | float: right; 246 | width: 810px; 247 | height: 50%; 248 | margin: 10px; 249 | padding: 15px; 250 | padding-top: 155px; 251 | font-family: 'Times New Roman', Times, serif; 252 | } 253 | 254 | .line2{ 255 | padding-top: 12px; 256 | } 257 | 258 | 259 | .bottom{ 260 | display: block; 261 | } 262 | 263 | .features2{ 264 | width: 100%; 265 | } 266 | .link{ 267 | background-color:rgb(#212529); 268 | } 269 | 270 | .rect{ 271 | float: right; 272 | width: 43%; 273 | margin: 0%; 274 | } 275 | .head{ 276 | float: right; 277 | } 278 | 279 | .para,.para2{ 280 | float: right; 281 | } 282 | 283 | .bottom3{ 284 | width:100%; 285 | height:50vh; 286 | margin-top:80px; 287 | 288 | } 289 | #quest{ 290 | color:black; 291 | text-align: center; 292 | font-family: sans-serif; 293 | } 294 | .questions{ 295 | font-size: 15px; 296 | font-family: sans-serif; 297 | } 298 | hr{ 299 | width:90%; 300 | } 301 | .qa{ 302 | margin-left:100px; 303 | margin-top:50px; 304 | } 305 | .answer{ 306 | display:none; 307 | } 308 | -------------------------------------------------------------------------------- /class_pratice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyNS0YJ78P2SvnFEW3LFFaMG", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "source": [ 32 | "class Car():\n", 33 | " def __init__(self,brand,model,color,full):\n", 34 | " self.full=full\n", 35 | " self.brand=brand\n", 36 | " self.model=model\n", 37 | " self.color=color\n", 38 | "\n", 39 | " def intro(self):\n", 40 | " print(f\"My Car Brand is {self.brand} and model is {self.model} and it's color is just wow {self.color}\")\n", 41 | "\n", 42 | " def full_tank(self):\n", 43 | " if self.full == True:\n", 44 | " print(\"Tank is full\")\n", 45 | " else:\n", 46 | " print(\"Tank is not full\")\n", 47 | "\n", 48 | "\n", 49 | "my_car=Car(\"audi\",\"a4\",\"black\",1)\n", 50 | "my_car.intro() # ---> First Method\n", 51 | "my_car.full_tank() # ---> Second Method\n", 52 | "\n", 53 | "\n", 54 | "print(my_car.model)\n", 55 | "print(my_car.brand)\n", 56 | "print(my_car.color)" 57 | ], 58 | "metadata": { 59 | "id": "9e5YPp3qY1Ku" 60 | }, 61 | "execution_count": null, 62 | "outputs": [] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "source": [ 67 | "🧠 Basic Level: Class and Object Creation\n", 68 | "✅ Create a class Car with attributes brand, model, and year. Write a method display_info() that prints the car details.\n", 69 | "\n", 70 | "✅ Create a class Student with attributes name, roll_no, and grade. Write a method display() to show student info.\n", 71 | "\n", 72 | "✅ Define a class Rectangle with attributes length and width. Add methods:\n", 73 | "\n", 74 | "area() to return the area.\n", 75 | "\n", 76 | "perimeter() to return the perimeter.\n", 77 | "\n", 78 | "✅ Create a class Circle with radius as attribute. Include:\n", 79 | "\n", 80 | "Method to calculate area.\n", 81 | "\n", 82 | "Method to calculate circumference.\n", 83 | "\n", 84 | "✅ Create a class BankAccount with attributes account_holder, balance. Add methods:\n", 85 | "\n", 86 | "deposit(amount)\n", 87 | "\n", 88 | "withdraw(amount)\n", 89 | "\n", 90 | "display_balance()" 91 | ], 92 | "metadata": { 93 | "id": "2kdr0C3scvVG" 94 | } 95 | }, 96 | { 97 | "cell_type": "code", 98 | "source": [ 99 | "class Student():\n", 100 | " def __init__(self,name,roll_no,grade):\n", 101 | " self.name=name\n", 102 | " self.roll_no=roll_no\n", 103 | " self.grade=grade\n", 104 | "\n", 105 | "\n", 106 | " def info(self):\n", 107 | " print( f\"Student Name: {self.name}\")\n", 108 | " print(f\"Student Roll_no: {self.roll_no}\")\n", 109 | " print(f\"Student Grade: {self.grade}\")\n", 110 | "\n", 111 | "\n", 112 | "\n", 113 | "My_info=Student(\"Manju\",25,\"A+\")\n", 114 | "My_info.info()" 115 | ], 116 | "metadata": { 117 | "colab": { 118 | "base_uri": "https://localhost:8080/" 119 | }, 120 | "id": "weC2nzANc9To", 121 | "outputId": "0e0c2a84-b203-466a-d4af-2c7b6a07344f" 122 | }, 123 | "execution_count": 16, 124 | "outputs": [ 125 | { 126 | "output_type": "stream", 127 | "name": "stdout", 128 | "text": [ 129 | "Student Name: Manju\n", 130 | "Student Roll_no: 25\n", 131 | "Student Grade: A+\n" 132 | ] 133 | } 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "source": [ 139 | "class Rectangle():\n", 140 | " def __init__(self,length,width):\n", 141 | " self.length=length\n", 142 | " self.width=width\n", 143 | "\n", 144 | " def area(self):\n", 145 | " return self.length*self.width\n", 146 | "\n", 147 | " def perimeter(self):\n", 148 | " return 2*(self.length+self.width)\n", 149 | "\n", 150 | "\n", 151 | "\n", 152 | "rec=Rectangle(10,20)\n", 153 | "print(\"Area of Rectangle : \",rec.area())\n", 154 | "print(\"Perimeter of Rectangle : \",rec.perimeter())\n" 155 | ], 156 | "metadata": { 157 | "colab": { 158 | "base_uri": "https://localhost:8080/" 159 | }, 160 | "id": "UFF6ElndezvC", 161 | "outputId": "86eb239c-d3a8-4bbf-8d88-7e56d518c9e3" 162 | }, 163 | "execution_count": 20, 164 | "outputs": [ 165 | { 166 | "output_type": "stream", 167 | "name": "stdout", 168 | "text": [ 169 | "Area of Rectangle : 200\n", 170 | "Perimeter of Rectangle : 60\n" 171 | ] 172 | } 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "source": [ 178 | "class Bank:\n", 179 | " def __init__(self, account_holder, balance):\n", 180 | " self.account_holder = account_holder\n", 181 | " self.balance = balance\n", 182 | "\n", 183 | " def deposit(self, amount):\n", 184 | " if amount > 0:\n", 185 | " self.balance += amount\n", 186 | " print(f\"Deposited ₹{amount}\")\n", 187 | " print(f'Total Balance {self.balance}')\n", 188 | " else:\n", 189 | " print(\"Invalid deposit amount.\")\n", 190 | "\n", 191 | " def withdraw(self, amount):\n", 192 | " if amount > 0 and amount <= self.balance:\n", 193 | " self.balance -= amount\n", 194 | " print(f\"Withdrawn ₹{amount}\")\n", 195 | " else:\n", 196 | " print(\"Invalid withdrawal amount or insufficient balance.\")\n", 197 | "\n", 198 | " def show_balance(self):\n", 199 | " print(f\"Available Balance: ₹{self.balance}\")\n", 200 | "\n", 201 | "\n", 202 | "bk = Bank(\"Manju\", 250000)\n", 203 | "bk.deposit(11000)\n", 204 | "print(\" \")\n", 205 | "bk.withdraw(5000)\n", 206 | "bk.show_balance()\n" 207 | ], 208 | "metadata": { 209 | "colab": { 210 | "base_uri": "https://localhost:8080/" 211 | }, 212 | "id": "UgYKm27sfmjx", 213 | "outputId": "4226b590-bf78-4bb4-f4cc-2f3ed6c1d3e9" 214 | }, 215 | "execution_count": 26, 216 | "outputs": [ 217 | { 218 | "output_type": "stream", 219 | "name": "stdout", 220 | "text": [ 221 | "Deposited ₹11000\n", 222 | "Total Balance 261000\n", 223 | " \n", 224 | "Withdrawn ₹5000\n", 225 | "Available Balance: ₹256000\n" 226 | ] 227 | } 228 | ] 229 | } 230 | ] 231 | } -------------------------------------------------------------------------------- /Practice_for_Day_6.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyOuJ6LEn7Q+NkZnrB+Ww3p1", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 7, 32 | "metadata": { 33 | "colab": { 34 | "base_uri": "https://localhost:8080/" 35 | }, 36 | "id": "Sug_xwYE3ghQ", 37 | "outputId": "051e9634-daf8-4f91-ded6-933219764d58" 38 | }, 39 | "outputs": [ 40 | { 41 | "output_type": "stream", 42 | "name": "stdout", 43 | "text": [ 44 | "First 5 Rows:\n", 45 | " PassengerId Survived Pclass ... Fare Cabin Embarked\n", 46 | "0 1 0 3 ... 7.2500 NaN S\n", 47 | "1 2 1 1 ... 71.2833 C85 C\n", 48 | "2 3 1 3 ... 7.9250 NaN S\n", 49 | "3 4 1 1 ... 53.1000 C123 S\n", 50 | "4 5 0 3 ... 8.0500 NaN S\n", 51 | "\n", 52 | "[5 rows x 13 columns]\n", 53 | "\n", 54 | " Missing Values Befroe Handling\n", 55 | "PassengerId 0\n", 56 | "Survived 0\n", 57 | "Pclass 0\n", 58 | "First Person 0\n", 59 | "Second Person 0\n", 60 | "Sex 0\n", 61 | "Age 177\n", 62 | "SibSp 0\n", 63 | "Parch 0\n", 64 | "Ticket 0\n", 65 | "Fare 0\n", 66 | "Cabin 687\n", 67 | "Embarked 2\n", 68 | "dtype: int64\n", 69 | "\n", 70 | "✅ Missing Values After Handling:\n", 71 | "PassengerId 0\n", 72 | "Survived 0\n", 73 | "Pclass 0\n", 74 | "First Person 0\n", 75 | "Second Person 0\n", 76 | "Sex 0\n", 77 | "Age 177\n", 78 | "SibSp 0\n", 79 | "Parch 0\n", 80 | "Ticket 0\n", 81 | "Fare 0\n", 82 | "Embarked 0\n", 83 | "dtype: int64\n", 84 | "\n", 85 | "🔢 Encoded Data Sample:\n", 86 | " Sex Embarked\n", 87 | "0 1 2\n", 88 | "1 0 0\n", 89 | "2 0 2\n", 90 | "3 0 2\n", 91 | "4 1 2\n", 92 | "\n", 93 | "📏 Scaled Data Sample:\n", 94 | " Age Fare\n", 95 | "0 -0.530377 -0.502445\n", 96 | "1 0.571831 0.786845\n", 97 | "2 -0.254825 -0.488854\n", 98 | "3 0.365167 0.420730\n", 99 | "4 0.365167 -0.486337\n", 100 | "\n", 101 | "💾 Preprocessed data saved to titanic_preprocessed.csv\n" 102 | ] 103 | }, 104 | { 105 | "output_type": "stream", 106 | "name": "stderr", 107 | "text": [ 108 | "/tmp/ipython-input-1085836434.py:15: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 109 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 110 | "\n", 111 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 112 | "\n", 113 | "\n", 114 | " df['Embarked'].fillna(df['Embarked'].mode()[0], inplace=True)\n" 115 | ] 116 | } 117 | ], 118 | "source": [ 119 | "import pandas as pd\n", 120 | "from sklearn.preprocessing import LabelEncoder,StandardScaler\n", 121 | "\n", 122 | "df = pd.read_csv('Titanic.csv')\n", 123 | "print('First 5 Rows:')\n", 124 | "print(df.head())\n", 125 | "\n", 126 | "# ========================\n", 127 | "# Step 1: Check Missing Values\n", 128 | "# ========================\n", 129 | "\n", 130 | "print(\"\\n Missing Values Befroe Handling\")\n", 131 | "print(df.isnull().sum())\n", 132 | "# Fill Embarked missing values with mode\n", 133 | "df['Embarked'].fillna(df['Embarked'].mode()[0], inplace=True)\n", 134 | "\n", 135 | "# Drop Cabin column (too many missing values)\n", 136 | "df.drop(columns=['Cabin'], inplace=True)\n", 137 | "\n", 138 | "print(\"\\n✅ Missing Values After Handling:\")\n", 139 | "print(df.isnull().sum())\n", 140 | "\n", 141 | "# ========================\n", 142 | "# Step 2: Encode Categorical Variables\n", 143 | "# ========================\n", 144 | "le = LabelEncoder()\n", 145 | "df['Sex'] = le.fit_transform(df['Sex']) # male=1, female=0\n", 146 | "df['Embarked'] = le.fit_transform(df['Embarked'])\n", 147 | "\n", 148 | "print(\"\\n🔢 Encoded Data Sample:\")\n", 149 | "print(df[['Sex', 'Embarked']].head())\n", 150 | "\n", 151 | "# ========================\n", 152 | "# Step 3: Feature Scaling\n", 153 | "# ========================\n", 154 | "scaler = StandardScaler()\n", 155 | "numeric_cols = ['Age', 'Fare']\n", 156 | "df[numeric_cols] = scaler.fit_transform(df[numeric_cols])\n", 157 | "\n", 158 | "print(\"\\n📏 Scaled Data Sample:\")\n", 159 | "print(df[numeric_cols].head())\n", 160 | "\n", 161 | "# ========================\n", 162 | "# Step 4: Save Preprocessed Data\n", 163 | "# ========================\n", 164 | "df.to_csv(\"titanic_preprocessed.csv\", index=False)\n", 165 | "print(\"\\n💾 Preprocessed data saved to titanic_preprocessed.csv\")" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "source": [], 171 | "metadata": { 172 | "id": "KQu_ee3M4RmU" 173 | }, 174 | "execution_count": null, 175 | "outputs": [] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "source": [ 180 | "import pandas as pd\n", 181 | "from sklearn.preprocessing import LabelEncoder , StandardScaler\n", 182 | "df = pd.read_csv('Student.csv')\n", 183 | "print(\"First 5 Rows\")\n", 184 | "print(df.head())\n", 185 | "print(\"\\n Missing Values before the handling\")\n", 186 | "print(df.isnull().sum())\n", 187 | "df['Age'].fillna(df['Age'].median(), inplace=True)\n", 188 | "df['Marks'].fillna(df['Marks'].mean(),inplace=True)\n", 189 | "df['Gender'].fillna(\"Unknown\", inplace=True)\n", 190 | "print(\"\\n✅ After Handling Nulls:\")\n", 191 | "print(df.isnull().sum())\n", 192 | "\n", 193 | "\n", 194 | "le=LabelEncoder()\n", 195 | "df['Gender']=le.fit_transform(df['Gender'])\n", 196 | "# print(\"\\n🔢 Encoded Data Sample:\")\n", 197 | "# print(df[['Gender']])\n", 198 | "\n", 199 | "df['Department'] = le.fit_transform(df['Department'])\n", 200 | "# print(\"\\n🔢 Encoded Data Sample:\")\n", 201 | "# print(df[['Department']])\n", 202 | "\n", 203 | "df=pd.get_dummies(df,columns=['City'],drop_first=True)\n", 204 | "# print(\"\\n🔢 Encoded Data Sample:\")\n", 205 | "# print(df)\n", 206 | "\n", 207 | "scaler=StandardScaler()\n", 208 | "numeric_cols=['Age','Marks']\n", 209 | "df[numeric_cols]=scaler.fit_transform(df[numeric_cols])\n", 210 | "print(\"\\n📏 Scaled Data Sample:\")\n", 211 | "print(df[numeric_cols].head())" 212 | ], 213 | "metadata": { 214 | "colab": { 215 | "base_uri": "https://localhost:8080/" 216 | }, 217 | "id": "bR5LNiLN8GXt", 218 | "outputId": "a21e39b5-dc99-458b-bd8f-20b736918fa6" 219 | }, 220 | "execution_count": 26, 221 | "outputs": [ 222 | { 223 | "output_type": "stream", 224 | "name": "stdout", 225 | "text": [ 226 | "First 5 Rows\n", 227 | " ID Name Gender Age Department Marks City\n", 228 | "0 1 Alice Female 20.0 Science 85.0 Chennai\n", 229 | "1 2 Bob Male 21.0 Commerce 78.0 Chennai\n", 230 | "2 3 Charlie Male NaN Science 92.0 Coimbatore\n", 231 | "3 4 David Male 23.0 Arts NaN Madurai\n", 232 | "4 5 Eva Female 22.0 Science 88.0 Chennai\n", 233 | "\n", 234 | " Missing Values before the handling\n", 235 | "ID 0\n", 236 | "Name 0\n", 237 | "Gender 1\n", 238 | "Age 2\n", 239 | "Department 0\n", 240 | "Marks 2\n", 241 | "City 0\n", 242 | "dtype: int64\n", 243 | "\n", 244 | "✅ After Handling Nulls:\n", 245 | "ID 0\n", 246 | "Name 0\n", 247 | "Gender 0\n", 248 | "Age 0\n", 249 | "Department 0\n", 250 | "Marks 0\n", 251 | "City 0\n", 252 | "dtype: int64\n", 253 | "\n", 254 | "📏 Scaled Data Sample:\n", 255 | " Age Marks\n", 256 | "0 -1.5 0.272664\n", 257 | "1 -0.5 -0.745282\n", 258 | "2 0.0 1.290610\n", 259 | "3 1.5 0.000000\n", 260 | "4 0.5 0.708927\n" 261 | ] 262 | }, 263 | { 264 | "output_type": "stream", 265 | "name": "stderr", 266 | "text": [ 267 | "/tmp/ipython-input-2300412600.py:8: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 268 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 269 | "\n", 270 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 271 | "\n", 272 | "\n", 273 | " df['Age'].fillna(df['Age'].median(), inplace=True)\n", 274 | "/tmp/ipython-input-2300412600.py:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 275 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 276 | "\n", 277 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 278 | "\n", 279 | "\n", 280 | " df['Marks'].fillna(df['Marks'].mean(),inplace=True)\n", 281 | "/tmp/ipython-input-2300412600.py:10: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", 282 | "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", 283 | "\n", 284 | "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", 285 | "\n", 286 | "\n", 287 | " df['Gender'].fillna(\"Unknown\", inplace=True)\n" 288 | ] 289 | } 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "source": [], 295 | "metadata": { 296 | "id": "VsI7KxqS9U6v" 297 | }, 298 | "execution_count": null, 299 | "outputs": [] 300 | } 301 | ] 302 | } -------------------------------------------------------------------------------- /Cross_Validation_Practice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyMFN5WIqT4632eAqfR/Chwm", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 35, 32 | "metadata": { 33 | "id": "hAU-0-Xl91RE" 34 | }, 35 | "outputs": [], 36 | "source": [ 37 | "import numpy as np\n", 38 | "import pandas as pd\n", 39 | "import matplotlib.pyplot as plt\n", 40 | "import warnings\n", 41 | "warnings.filterwarnings('ignore')\n", 42 | "\n", 43 | "from sklearn.tree import DecisionTreeRegressor\n", 44 | "from sklearn.model_selection import train_test_split,cross_val_score,KFold\n", 45 | "from sklearn.metrics import accuracy_score,classification_report\n", 46 | "from sklearn.preprocessing import StandardScaler\n", 47 | "\n", 48 | "\n", 49 | "df=pd.read_csv(\"/content/sample_data/california_housing_train.csv\")\n", 50 | "\n", 51 | "X=df.drop('median_house_value',axis=1)\n", 52 | "y=df['median_house_value']\n", 53 | "\n", 54 | "scaler = StandardScaler()\n", 55 | "X_scaled= scaler.fit_transform(X)\n", 56 | "\n", 57 | "model = DecisionTreeRegressor(random_state=42)\n", 58 | "\n" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "source": [ 64 | "kf=KFold(n_splits=5,shuffle=True,random_state=42)\n", 65 | "cv=cross_val_score(model,X_scaled,y,cv=kf,scoring='r2')\n", 66 | "print(f\"Accuracy:{cv.mean()}\")" 67 | ], 68 | "metadata": { 69 | "colab": { 70 | "base_uri": "https://localhost:8080/" 71 | }, 72 | "id": "4qVl5aRCBidx", 73 | "outputId": "c7f53521-7511-46e8-8181-d5e76bb5e5b5" 74 | }, 75 | "execution_count": 39, 76 | "outputs": [ 77 | { 78 | "output_type": "stream", 79 | "name": "stdout", 80 | "text": [ 81 | "Accuracy:0.6419646344238668\n" 82 | ] 83 | } 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "source": [ 89 | "import numpy as np\n", 90 | "import pandas as pd\n", 91 | "import warnings\n", 92 | "warnings.filterwarnings('ignore')\n", 93 | "\n", 94 | "from sklearn.ensemble import RandomForestRegressor\n", 95 | "from sklearn.model_selection import KFold, cross_val_score\n", 96 | "from sklearn.preprocessing import StandardScaler\n", 97 | "\n", 98 | "# Load dataset\n", 99 | "df = pd.read_csv(\"/content/sample_data/california_housing_train.csv\")\n", 100 | "\n", 101 | "# Features & target\n", 102 | "X = df.drop('median_house_value', axis=1)\n", 103 | "y = df['median_house_value']\n", 104 | "\n", 105 | "# Scale features\n", 106 | "scaler = StandardScaler()\n", 107 | "X_scaled = scaler.fit_transform(X)\n", 108 | "\n", 109 | "# Model\n", 110 | "model = RandomForestRegressor(random_state=42)\n", 111 | "\n", 112 | "# Cross Validation\n", 113 | "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", 114 | "cv = cross_val_score(model, X_scaled, y, cv=kf, scoring='r2')\n", 115 | "\n", 116 | "print(\"Cross Validation R² Scores:\", cv)\n", 117 | "print(\"Average R² Score:\", np.mean(cv))\n" 118 | ], 119 | "metadata": { 120 | "colab": { 121 | "base_uri": "https://localhost:8080/" 122 | }, 123 | "id": "XhSFJdAAB98P", 124 | "outputId": "491b77b9-bc38-4b03-c3ea-b82ccc2bd522" 125 | }, 126 | "execution_count": 34, 127 | "outputs": [ 128 | { 129 | "output_type": "stream", 130 | "name": "stdout", 131 | "text": [ 132 | "Cross Validation R² Scores: [0.82221567 0.81649232 0.82249634 0.83059332 0.81128691]\n", 133 | "Average R² Score: 0.8206169125484534\n" 134 | ] 135 | } 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "source": [ 141 | "# Exercise: Machine Learning Finding Optimal Model and Hyperparameters\n", 142 | "# For digits dataset in sklearn.dataset, please try following classifiers and find out the one that gives best performance. Also find the optimal parameters for that classifier.\n", 143 | "\n", 144 | "# from sklearn import svm\n", 145 | "# from sklearn.ensemble import RandomForestClassifier\n", 146 | "# from sklearn.linear_model import LogisticRegression\n", 147 | "# from sklearn.naive_bayes import GaussianNB\n", 148 | "# from sklearn.naive_bayes import MultinomialNB\n", 149 | "# from sklearn.tree import DecisionTreeClassifier" 150 | ], 151 | "metadata": { 152 | "id": "36lotqPxJg86" 153 | }, 154 | "execution_count": 50, 155 | "outputs": [] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "source": [ 160 | "from sklearn import svm\n", 161 | "from sklearn.ensemble import RandomForestClassifier\n", 162 | "from sklearn.linear_model import LogisticRegression\n", 163 | "from sklearn.naive_bayes import GaussianNB\n", 164 | "from sklearn.naive_bayes import MultinomialNB\n", 165 | "from sklearn.tree import DecisionTreeClassifier\n", 166 | "\n", 167 | "from sklearn import datasets\n", 168 | "digits = datasets.load_digits()\n", 169 | "\n", 170 | "\n", 171 | "model_params = {\n", 172 | " 'svm': {\n", 173 | " 'model': svm.SVC(gamma='auto'),\n", 174 | " 'params' : {\n", 175 | " 'C': [1,10,20],\n", 176 | " 'kernel': ['rbf','linear']\n", 177 | " }\n", 178 | " },\n", 179 | " 'random_forest': {\n", 180 | " 'model': RandomForestClassifier(),\n", 181 | " 'params' : {\n", 182 | " 'n_estimators': [1,5,10]\n", 183 | " }\n", 184 | " },\n", 185 | " 'logistic_regression' : {\n", 186 | " 'model': LogisticRegression(solver='liblinear',multi_class='auto'),\n", 187 | " 'params': {\n", 188 | " 'C': [1,5,10]\n", 189 | " }\n", 190 | " },\n", 191 | " 'naive_bayes_gaussian': {\n", 192 | " 'model': GaussianNB(),\n", 193 | " 'params': {}\n", 194 | " },\n", 195 | " 'naive_bayes_multinomial': {\n", 196 | " 'model': MultinomialNB(),\n", 197 | " 'params': {}\n", 198 | " },\n", 199 | " 'decision_tree': {\n", 200 | " 'model': DecisionTreeClassifier(),\n", 201 | " 'params': {\n", 202 | " 'criterion': ['gini','entropy'],\n", 203 | "\n", 204 | " }\n", 205 | " }\n", 206 | "}\n", 207 | "\n", 208 | "\n", 209 | "\n", 210 | "from sklearn.model_selection import GridSearchCV\n", 211 | "import pandas as pd\n", 212 | "scores = []\n", 213 | "\n", 214 | "\n", 215 | "\n", 216 | "for model_name,mp in model_params.items():\n", 217 | " clf=GridSearchCV(mp['model'],mp['params'],cv=5,return_train_score=False)\n", 218 | " clf.fit(digits.data,digits.target)\n", 219 | " scores.append({\n", 220 | " 'model':model_name,\n", 221 | " 'best_score':clf.best_score_,\n", 222 | " 'best_params':clf.best_params_\n", 223 | " })\n", 224 | "\n", 225 | "\n", 226 | "df= pd.DataFrame(scores,columns=['model','best_params','best_score'])\n", 227 | "df" 228 | ], 229 | "metadata": { 230 | "colab": { 231 | "base_uri": "https://localhost:8080/", 232 | "height": 237 233 | }, 234 | "id": "ZoSeFgQxGReV", 235 | "outputId": "f8b39a26-fa2b-47cf-829f-8cf58e7c1ebf" 236 | }, 237 | "execution_count": 51, 238 | "outputs": [ 239 | { 240 | "output_type": "execute_result", 241 | "data": { 242 | "text/plain": [ 243 | " model best_params best_score\n", 244 | "0 svm {'C': 1, 'kernel': 'linear'} 0.947697\n", 245 | "1 random_forest {'n_estimators': 10} 0.914872\n", 246 | "2 logistic_regression {'C': 1} 0.922114\n", 247 | "3 naive_bayes_gaussian {} 0.806928\n", 248 | "4 naive_bayes_multinomial {} 0.870350\n", 249 | "5 decision_tree {'criterion': 'entropy'} 0.815268" 250 | ], 251 | "text/html": [ 252 | "\n", 253 | "

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3	naive_bayes_gaussian	{}	0.806928
4	naive_bayes_multinomial	{}	0.870350
5	decision_tree	{'criterion': 'entropy'}	0.815268

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\n" 581 | ], 582 | "application/vnd.google.colaboratory.intrinsic+json": { 583 | "type": "dataframe", 584 | "variable_name": "df", 585 | "summary": "{\n \"name\": \"df\",\n \"rows\": 6,\n \"fields\": [\n {\n \"column\": \"model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"svm\",\n \"random_forest\",\n \"decision_tree\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"best_params\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"best_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05864151308007648,\n \"min\": 0.8069281956050759,\n \"max\": 0.9476973073351903,\n \"num_unique_values\": 6,\n \"samples\": [\n 0.9476973073351903,\n 0.9148715567935624,\n 0.8152677189724544\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" 586 | } 587 | }, 588 | "metadata": {}, 589 | "execution_count": 51 590 | } 591 | ] 592 | } 593 | ] 594 | } -------------------------------------------------------------------------------- /Logistice_Regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyM6fZ6/wtva69xuSU4wXoti", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | " $\"Open$ " 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": { 33 | "id": "4T5K-wVEsyWE" 34 | }, 35 | "outputs": [], 36 | "source": [ 37 | "import pandas as pd\n", 38 | "import numpy as np\n", 39 | "import seaborn as sns\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "\n", 42 | "df=pd.read_csv(\"framingham_heart_disease.csv\")\n", 43 | "df.head()" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "source": [ 49 | "df.isnull().sum()" 50 | ], 51 | "metadata": { 52 | "id": "jBBWdnZ3taS1" 53 | }, 54 | "execution_count": null, 55 | "outputs": [] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "source": [ 60 | "df= df.drop(['education'],axis=1)" 61 | ], 62 | "metadata": { 63 | "id": "oJStLTG8vu8R" 64 | }, 65 | "execution_count": 6, 66 | "outputs": [] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "source": [ 71 | "#Lets find out the mean values for replacing with missing values\n", 72 | "cigsPerDay_mean = round(df[\"cigsPerDay\"].mean())\n", 73 | "BPMeds_mean = round(df[\"BPMeds\"].mean())\n", 74 | "totChol_mean = round(df[\"totChol\"].mean())\n", 75 | "BMI_mean = round(df[\"BMI\"].mean())\n", 76 | "heartRate_mean = round(df[\"heartRate\"].mean())\n", 77 | "glucose_mean = round(df[\"glucose\"].mean())" 78 | ], 79 | "metadata": { 80 | "id": "u6zYXOWHv5bY" 81 | }, 82 | "execution_count": 10, 83 | "outputs": [] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "source": [ 88 | "df['cigsPerDay'].fillna(cigsPerDay_mean,inplace=True)\n", 89 | "df['BPMeds'].fillna(BPMeds_mean, inplace = True)\n", 90 | "df['totChol'].fillna(totChol_mean, inplace = True)\n", 91 | "df['BMI'].fillna(BMI_mean, inplace = True)\n", 92 | "df['heartRate'].fillna(heartRate_mean, inplace = True)\n", 93 | "df['glucose'].fillna(glucose_mean, inplace = True)" 94 | ], 95 | "metadata": { 96 | "id": "4Ghft7GwwHqE" 97 | }, 98 | "execution_count": null, 99 | "outputs": [] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "source": [ 104 | "X= df.drop('TenYearCHD',axis=1)\n", 105 | "\n", 106 | "y=df['TenYearCHD']" 107 | ], 108 | "metadata": { 109 | "id": "W2Ivib8IwlHu" 110 | }, 111 | "execution_count": 20, 112 | "outputs": [] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "source": [ 117 | "from sklearn.model_selection import train_test_split\n", 118 | "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.75,random_state=42)\n", 119 | "\n", 120 | "\n", 121 | "print(X.shape)\n", 122 | "print(y.shape)\n", 123 | "print(X_train.shape)\n", 124 | "print(X_test.shape)\n", 125 | "print(y_train.shape)\n", 126 | "print(y_test.shape)" 127 | ], 128 | "metadata": { 129 | "colab": { 130 | "base_uri": "https://localhost:8080/" 131 | }, 132 | "id": "UfD7Bnadwxn_", 133 | "outputId": "3886e6c6-6952-4019-e866-9e923cb1fafb" 134 | }, 135 | "execution_count": 23, 136 | "outputs": [ 137 | { 138 | "output_type": "stream", 139 | "name": "stdout", 140 | "text": [ 141 | "(4238, 14)\n", 142 | "(4238,)\n", 143 | "(3178, 14)\n", 144 | "(1060, 14)\n", 145 | "(3178,)\n", 146 | "(1060,)\n" 147 | ] 148 | } 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "source": [ 154 | "from sklearn.linear_model import LogisticRegression\n", 155 | "\n", 156 | "classifier= LogisticRegression(class_weight='balanced')\n", 157 | "classifier.fit(X_train,y_train)" 158 | ], 159 | "metadata": { 160 | "colab": { 161 | "base_uri": "https://localhost:8080/", 162 | "height": 219 163 | }, 164 | "id": "NafeYLNjxe1f", 165 | "outputId": "f1b54ab3-ee5e-4f52-8816-764bee43bb12" 166 | }, 167 | "execution_count": 31, 168 | "outputs": [ 169 | { 170 | "output_type": "stream", 171 | "name": "stderr", 172 | "text": [ 173 | "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", 174 | "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", 175 | "\n", 176 | "Increase the number of iterations (max_iter) or scale the data as shown in:\n", 177 | " https://scikit-learn.org/stable/modules/preprocessing.html\n", 178 | "Please also refer to the documentation for alternative solver options:\n", 179 | " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", 180 | " n_iter_i = _check_optimize_result(\n" 181 | ] 182 | }, 183 | { 184 | "output_type": "execute_result", 185 | "data": { 186 | "text/plain": [ 187 | "LogisticRegression(class_weight='balanced')" 188 | ], 189 | "text/html": [ 190 | "

LogisticRegression(class_weight='balanced')

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" 606 | ] 607 | }, 608 | "metadata": {}, 609 | "execution_count": 31 610 | } 611 | ] 612 | }, 613 | { 614 | "cell_type": "code", 615 | "source": [ 616 | "from sklearn.metrics import accuracy_score\n", 617 | "\n", 618 | "X_Train_Prediction = classifier.predict(X_train)\n", 619 | "training_data_accuracy = accuracy_score(X_Train_Prediction, y_train)\n", 620 | "print('Accuracy score of the training data : ', training_data_accuracy)" 621 | ], 622 | "metadata": { 623 | "colab": { 624 | "base_uri": "https://localhost:8080/" 625 | }, 626 | "id": "GWCa-YuhxrWA", 627 | "outputId": "a3deb494-74ee-4896-eed8-d9b2450e04bf" 628 | }, 629 | "execution_count": 33, 630 | "outputs": [ 631 | { 632 | "output_type": "stream", 633 | "name": "stdout", 634 | "text": [ 635 | "Accuracy score of the training data : 0.6526117054751416\n" 636 | ] 637 | } 638 | ] 639 | }, 640 | { 641 | "cell_type": "code", 642 | "source": [ 643 | "# accuracy on test data\n", 644 | "X_test_prediction = classifier.predict(X_test)\n", 645 | "test_data_accuracy = accuracy_score(X_test_prediction, y_test)\n", 646 | "\n", 647 | "print('Accuracy on Test data : ', test_data_accuracy)" 648 | ], 649 | "metadata": { 650 | "colab": { 651 | "base_uri": "https://localhost:8080/" 652 | }, 653 | "id": "aKmOFH53yLvz", 654 | "outputId": "3fd5d27b-eaf9-4486-8022-36399adaad61" 655 | }, 656 | "execution_count": 34, 657 | "outputs": [ 658 | { 659 | "output_type": "stream", 660 | "name": "stdout", 661 | "text": [ 662 | "Accuracy on Test data : 0.6320754716981132\n" 663 | ] 664 | } 665 | ] 666 | }, 667 | { 668 | "cell_type": "code", 669 | "source": [ 670 | "#Lets print the classification report\n", 671 | "from sklearn.metrics import classification_report\n", 672 | "\n", 673 | "print(classification_report(y_test, X_test_prediction))" 674 | ], 675 | "metadata": { 676 | "colab": { 677 | "base_uri": "https://localhost:8080/" 678 | }, 679 | "id": "az522fqSyV9Z", 680 | "outputId": "df1963a6-a746-495f-8013-dd85453b519f" 681 | }, 682 | "execution_count": 35, 683 | "outputs": [ 684 | { 685 | "output_type": "stream", 686 | "name": "stdout", 687 | "text": [ 688 | " precision recall f1-score support\n", 689 | "\n", 690 | " 0 0.92 0.63 0.75 908\n", 691 | " 1 0.23 0.66 0.34 152\n", 692 | "\n", 693 | " accuracy 0.63 1060\n", 694 | " macro avg 0.57 0.64 0.54 1060\n", 695 | "weighted avg 0.82 0.63 0.69 1060\n", 696 | "\n" 697 | ] 698 | } 699 | ] 700 | } 701 | ] 702 | } -------------------------------------------------------------------------------- /Linear_Rgression_Basics_Prg_.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "toc_visible": true, 8 | "authorship_tag": "ABX9TyPdaga/wyMDEPmNX/C/x8gJ", 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "language_info": { 16 | "name": "python" 17 | } 18 | }, 19 | "cells": [ 20 | { 21 | "cell_type": "markdown", 22 | "metadata": { 23 | "id": "view-in-github", 24 | "colab_type": "text" 25 | }, 26 | "source": [ 27 | " $\"Open$ " 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 10, 33 | "metadata": { 34 | "colab": { 35 | "base_uri": "https://localhost:8080/" 36 | }, 37 | "id": "54IhFr7KZwr7", 38 | "outputId": "f86675c4-7289-4ad0-a378-158ac20b3b8c" 39 | }, 40 | "outputs": [ 41 | { 42 | "output_type": "stream", 43 | "name": "stdout", 44 | "text": [ 45 | "(5, 1)\n", 46 | "(5,)\n", 47 | "If the Student study for 8 hours he score mark of: 65\n" 48 | ] 49 | } 50 | ], 51 | "source": [ 52 | "import numpy as np\n", 53 | "from sklearn.linear_model import LinearRegression\n", 54 | "import matplotlib.pyplot as plt\n", 55 | "\n", 56 | "hours = np.array([[1],[2],[3],[4],[5]])\n", 57 | "marks=np.array([30,35,40,45,50])\n", 58 | "print(hours.shape)\n", 59 | "print(marks.shape)\n", 60 | "\n", 61 | "model = LinearRegression()\n", 62 | "model.fit(hours,marks)\n", 63 | "\n", 64 | "predict= model.predict([[8]])\n", 65 | "\n", 66 | "print(f\"If the Student study for 8 hours he score mark of: {round(predict[0])}\")" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "source": [ 72 | "pred= model.predict(hours)\n", 73 | "plt.figure(figsize=(8,5))\n", 74 | "plt.scatter(hours,marks, color='blue',label = \"Actual Marks\")\n", 75 | "plt.plot(hours,pred,color='red',label=\"Predicted Marks\")\n", 76 | "plt.scatter(8, predict, color='green', label='Prediction for 8 hrs', marker='o', s=100)\n", 77 | "plt.xlabel(\"Hours\")\n", 78 | "plt.ylabel(\"Marks\")\n", 79 | "plt.legend()\n", 80 | "plt.grid(True)\n", 81 | "plt.show()" 82 | ], 83 | "metadata": { 84 | "colab": { 85 | "base_uri": "https://localhost:8080/", 86 | "height": 465 87 | }, 88 | "id": "hirmEWlSbVlV", 89 | "outputId": "9020021e-9b47-4787-ec96-48d03259e9c7" 90 | }, 91 | "execution_count": 19, 92 | "outputs": [ 93 | { 94 | "output_type": "display_data", 95 | "data": { 96 | "text/plain": [ 97 | "

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