{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f605e499-bbd0-4c97-b548-e5623dcb82fb", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_breast_cancer ,load_iris , fetch_openml ,fetch_california_housing\n", "from sklearn.decomposition import PCA\n", "from sklearn.metrics import accuracy_score,recall_score,f1_score,precision_score\n", "from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error,root_mean_squared_error\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import cross_val_predict\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import OrdinalEncoder\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.datasets import make_blobs,make_moons\n", "from sklearn.cluster import KMeans,DBSCAN\n", "from sklearn.pipeline import Pipeline\n", "\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "fcc1baa2-0748-4fbe-a1b7-bab9e5551ac3", "metadata": {}, "outputs": [], "source": [ "X,y = load_breast_cancer(return_X_y = True )" ] }, { "cell_type": "code", "execution_count": 3, "id": "5bee3e57-d515-4703-892a-6efdb677cd86", "metadata": {}, "outputs": [], "source": [ "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)" ] }, { "cell_type": "code", "execution_count": 4, "id": "76f30223-a9ad-4a70-bfd3-9673bfe10532", "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()" ] }, { "cell_type": "code", "execution_count": 5, "id": "ebbb7e26-3331-4ad8-a792-9d493603b06c", "metadata": {}, "outputs": [], "source": [ "X_train_scaler = scaler.fit_transform(X_train)\n", "X_test_scaler = scaler.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 6, "id": "78f4ae35-fb8c-47b0-85fe-fad662f13b97", "metadata": {}, "outputs": [], "source": [ "knn = KNeighborsClassifier()" ] }, { "cell_type": "code", "execution_count": 7, "id": "1b9012cf-d855-4f03-aac7-ea1cb594d362", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "KNeighborsClassifier()" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(X_train_scaler,y_train)" ] }, { "cell_type": "code", "execution_count": 8, "id": "15590e0a-8674-4dd1-8f9c-83ed9cefdedf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9824561403508771" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.score(X_test_scaler,y_test)" ] }, { "cell_type": "code", "execution_count": 9, "id": "2fa51362-d4f1-46df-b0b5-4a02241af0b2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n", " 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n", " 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,\n", " 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n", " 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n", " 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n", " 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n", " 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,\n", " 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n", " 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,\n", " 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n", " 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n", " 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n", " 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n", " 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "code", "execution_count": 10, "id": "f80f97b9-32ec-4ad5-9ef7-eb4ad0e857d6", "metadata": {}, "outputs": [], "source": [ " X,y = load_iris(return_X_y = True)" ] }, { "cell_type": "code", "execution_count": 11, "id": "84a10c20-6655-4754-b415-5762992ea759", "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()" ] }, { "cell_type": "code", "execution_count": 12, "id": "1fc42225-a5cd-43c2-b282-b1ccab45df0a", "metadata": {}, "outputs": [], "source": [ "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)" ] }, { "cell_type": "code", "execution_count": 13, "id": "5c68cb3e-d738-42df-bf58-c1cc724a8630", "metadata": {}, "outputs": [], "source": [ "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 14, "id": "5c2caa89-26f7-4ab6-8a7f-9d4f51618282", "metadata": {}, "outputs": [], "source": [ "#what scaler do is like this\n", "xscaled = X_train - np.mean(X_train ,axis = 0) # here axis means that it will do mean at a particular area like 1-2,2-3" ] }, { "cell_type": "code", "execution_count": null, "id": "25533cf0-c9c6-453d-b9f1-c4a644f4b7e5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "id": "2ed34076-7140-4152-86e1-563c22178cee", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "outputs": [], "source": [ "scaler = MinMaxScaler() #it basically never get a negetive value\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 16, "id": "f251fae5-a213-4ffc-bf30-3fad38e1f2d8", "metadata": {}, "outputs": [], "source": [ "#we can also do this bu numpy\n", "X_min = np.min(X_train , axis = 0)\n", "X_max = np.max(X_train , axis = 0)\n", "\n", "minmaxscaled = (X_train - X_min)/(X_max - X_min)\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "596db169-07a3-444a-a3a0-47cb6dbc6438", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\datasets\\_openml.py:328: UserWarning: Multiple active versions of the dataset matching the name car exist. Versions may be fundamentally different, returning version 2. Available versions:\n", "- version 2, status: active\n", " url: https://www.openml.org/search?type=data&id=991\n", "- version 3, status: active\n", " url: https://www.openml.org/search?type=data&id=40975\n", "\n", " warn(warning_msg)\n" ] } ], "source": [ "data = fetch_openml('car' , as_frame = True).frame" ] }, { "cell_type": "code", "execution_count": 18, "id": "780e1fe6-dbb0-4328-a70e-8146fe620ddb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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buyingmaintdoorspersonslug_bootsafetybinaryClass
0vhighvhigh22smalllowP
1vhighvhigh22smallmedP
2vhighvhigh22smallhighP
3vhighvhigh22medlowP
4vhighvhigh22medmedP
........................
1723lowlow5moremoremedmedN
1724lowlow5moremoremedhighN
1725lowlow5moremorebiglowP
1726lowlow5moremorebigmedN
1727lowlow5moremorebighighN
\n", "

1728 rows × 7 columns

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" ], "text/plain": [ " buying maint doors persons lug_boot safety binaryClass\n", "0 vhigh vhigh 2 2 small low P\n", "1 vhigh vhigh 2 2 small med P\n", "2 vhigh vhigh 2 2 small high P\n", "3 vhigh vhigh 2 2 med low P\n", "4 vhigh vhigh 2 2 med med P\n", "... ... ... ... ... ... ... ...\n", "1723 low low 5more more med med N\n", "1724 low low 5more more med high N\n", "1725 low low 5more more big low P\n", "1726 low low 5more more big med N\n", "1727 low low 5more more big high N\n", "\n", "[1728 rows x 7 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 19, "id": "9694fae1-e3f3-403c-9d9d-40eef194dffa", "metadata": {}, "outputs": [], "source": [ "column_to_encode = [ 'lug_boot','safety']\n", "encoder = OrdinalEncoder(#it changes the data to numeric\n", " categories = [\n", " ['small','med','big'],\n", " ['low','med','high'],\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "id": "8a83b900-b1fb-4186-8813-06db8e60a313", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " buying maint doors persons binaryClass lug_boot safety\n", "0 vhigh vhigh 2 2 P 2.0 1.0\n", "1 vhigh vhigh 2 2 P 2.0 2.0\n", "2 vhigh vhigh 2 2 P 2.0 0.0\n", "3 vhigh vhigh 2 2 P 1.0 1.0\n", "4 vhigh vhigh 2 2 P 1.0 2.0\n" ] } ], "source": [ "from sklearn.preprocessing import OrdinalEncoder\n", "\n", "# Select the columns you want to encode\n", "columns_to_encode = [\"lug_boot\", \"safety\"]\n", "\n", "# Make sure they are strings\n", "data[columns_to_encode] = data[columns_to_encode].astype(str)\n", "\n", "# Initialize the encoder\n", "encoder = OrdinalEncoder()\n", "\n", "# Fit + transform\n", "encoded = encoder.fit_transform(data[columns_to_encode])\n", "\n", "# Put back into DataFrame\n", "encoded_df = pd.DataFrame(encoded, columns=columns_to_encode, index=data.index)\n", "\n", "# Replace original columns with encoded ones\n", "data = data.drop(columns=columns_to_encode).join(encoded_df)\n", "\n", "print(data.head())\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "2bb08212-8a02-45c2-8f66-e7437c025973", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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buyingmaintdoorspersonsbinaryClasslug_bootsafety
0vhighvhigh22P2.01.0
1vhighvhigh22P2.02.0
2vhighvhigh22P2.00.0
3vhighvhigh22P1.01.0
4vhighvhigh22P1.02.0
........................
1723lowlow5moremoreN1.02.0
1724lowlow5moremoreN1.00.0
1725lowlow5moremoreP0.01.0
1726lowlow5moremoreN0.02.0
1727lowlow5moremoreN0.00.0
\n", "

1728 rows × 7 columns

\n", "
" ], "text/plain": [ " buying maint doors persons binaryClass lug_boot safety\n", "0 vhigh vhigh 2 2 P 2.0 1.0\n", "1 vhigh vhigh 2 2 P 2.0 2.0\n", "2 vhigh vhigh 2 2 P 2.0 0.0\n", "3 vhigh vhigh 2 2 P 1.0 1.0\n", "4 vhigh vhigh 2 2 P 1.0 2.0\n", "... ... ... ... ... ... ... ...\n", "1723 low low 5more more N 1.0 2.0\n", "1724 low low 5more more N 1.0 0.0\n", "1725 low low 5more more P 0.0 1.0\n", "1726 low low 5more more N 0.0 2.0\n", "1727 low low 5more more N 0.0 0.0\n", "\n", "[1728 rows x 7 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 22, "id": "865c7232-f559-49d9-9f78-b75e0a211ee5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " lug_boot safety\n", "0 small low\n", "1 small med\n", "2 small high\n", "3 med low\n", "4 med med\n" ] } ], "source": [ "# Columns to decode\n", "cols_to_decode = [\"lug_boot\", \"safety\"]\n", "\n", "decoded_df = encoded_df.copy()\n", "\n", "for col in cols_to_decode:\n", " # Find column index in encoder\n", " col_idx = list(encoded_df.columns).index(col)\n", " \n", " # Reverse mapping\n", " mapping = dict(enumerate(encoder.categories_[col_idx]))\n", " \n", " # Convert float -> int before mapping\n", " decoded_df[col] = encoded_df[col].astype(int).map(mapping)\n", "\n", "print(decoded_df[cols_to_decode].head())\n" ] }, { "cell_type": "code", "execution_count": 37, "id": "0d343b54-0ed6-40fd-b6b8-61fff5893157", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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buyingmaintdoorspersonsbinaryClasslug_bootsafety
0vhighvhigh22P2.01.0
1vhighvhigh22P2.02.0
2vhighvhigh22P2.00.0
3vhighvhigh22P1.01.0
4vhighvhigh22P1.02.0
........................
1723lowlow5moremoreN1.02.0
1724lowlow5moremoreN1.00.0
1725lowlow5moremoreP0.01.0
1726lowlow5moremoreN0.02.0
1727lowlow5moremoreN0.00.0
\n", "

1728 rows × 7 columns

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" ], "text/plain": [ " buying maint doors persons binaryClass lug_boot safety\n", "0 vhigh vhigh 2 2 P 2.0 1.0\n", "1 vhigh vhigh 2 2 P 2.0 2.0\n", "2 vhigh vhigh 2 2 P 2.0 0.0\n", "3 vhigh vhigh 2 2 P 1.0 1.0\n", "4 vhigh vhigh 2 2 P 1.0 2.0\n", "... ... ... ... ... ... ... ...\n", "1723 low low 5more more N 1.0 2.0\n", "1724 low low 5more more N 1.0 0.0\n", "1725 low low 5more more P 0.0 1.0\n", "1726 low low 5more more N 0.0 2.0\n", "1727 low low 5more more N 0.0 0.0\n", "\n", "[1728 rows x 7 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 38, "id": "894f284f-b70f-4bb1-8666-bdc6bb843029", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\datasets\\_openml.py:328: UserWarning: Multiple active versions of the dataset matching the name adult exist. Versions may be fundamentally different, returning version 1. Available versions:\n", "- version 1, status: active\n", " url: https://www.openml.org/search?type=data&id=179\n", "- version 2, status: active\n", " url: https://www.openml.org/search?type=data&id=1590\n", "\n", " warn(warning_msg)\n" ] } ], "source": [ "data = fetch_openml('adult',as_frame = True).frame" ] }, { "cell_type": "code", "execution_count": 39, "id": "b6081333-9272-443e-8038-04ca314fee06", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapitalgaincapitallosshoursperweeknative-countryclass
02State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale102United-States<=50K
13Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale000United-States<=50K
22Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale002United-States<=50K
33Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale002United-States<=50K
41Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale002Cuba<=50K
................................................
488372Private215419Bachelors13DivorcedProf-specialtyNot-in-familyWhiteFemale002United-States<=50K
488384NaN321403HS-grad9WidowedNaNOther-relativeBlackMale002United-States<=50K
488392Private374983Bachelors13Married-civ-spouseProf-specialtyHusbandWhiteMale003United-States<=50K
488402Private83891Bachelors13DivorcedAdm-clericalOwn-childAsian-Pac-IslanderMale202United-States<=50K
488411Self-emp-inc182148Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale003United-States>50K
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48842 rows × 15 columns

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" ], "text/plain": [ " age workclass fnlwgt education education-num \\\n", "0 2 State-gov 77516 Bachelors 13 \n", "1 3 Self-emp-not-inc 83311 Bachelors 13 \n", "2 2 Private 215646 HS-grad 9 \n", "3 3 Private 234721 11th 7 \n", "4 1 Private 338409 Bachelors 13 \n", "... .. ... ... ... ... \n", "48837 2 Private 215419 Bachelors 13 \n", "48838 4 NaN 321403 HS-grad 9 \n", "48839 2 Private 374983 Bachelors 13 \n", "48840 2 Private 83891 Bachelors 13 \n", "48841 1 Self-emp-inc 182148 Bachelors 13 \n", "\n", " marital-status occupation relationship \\\n", "0 Never-married Adm-clerical Not-in-family \n", "1 Married-civ-spouse Exec-managerial Husband \n", "2 Divorced Handlers-cleaners Not-in-family \n", "3 Married-civ-spouse Handlers-cleaners Husband \n", "4 Married-civ-spouse Prof-specialty Wife \n", "... ... ... ... \n", "48837 Divorced Prof-specialty Not-in-family \n", "48838 Widowed NaN Other-relative \n", "48839 Married-civ-spouse Prof-specialty Husband \n", "48840 Divorced Adm-clerical Own-child \n", "48841 Married-civ-spouse Exec-managerial Husband \n", "\n", " race sex capitalgain capitalloss hoursperweek \\\n", "0 White Male 1 0 2 \n", "1 White Male 0 0 0 \n", "2 White Male 0 0 2 \n", "3 Black Male 0 0 2 \n", "4 Black Female 0 0 2 \n", "... ... ... ... ... ... \n", "48837 White Female 0 0 2 \n", "48838 Black Male 0 0 2 \n", "48839 White Male 0 0 3 \n", "48840 Asian-Pac-Islander Male 2 0 2 \n", "48841 White Male 0 0 3 \n", "\n", " native-country class \n", "0 United-States <=50K \n", "1 United-States <=50K \n", "2 United-States <=50K \n", "3 United-States <=50K \n", "4 Cuba <=50K \n", "... ... ... \n", "48837 United-States <=50K \n", "48838 United-States <=50K \n", "48839 United-States <=50K \n", "48840 United-States <=50K \n", "48841 United-States >50K \n", "\n", "[48842 rows x 15 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 40, "id": "6692f4a2-74a1-4682-8753-faa345a4e3e1", "metadata": {}, "outputs": [], "source": [ "encoder = OneHotEncoder(handle_unknown = 'ignore',sparse_output= False)" ] }, { "cell_type": "code", "execution_count": 41, "id": "dd394b57-9aad-42be-95dd-181c31c0e23e", "metadata": {}, "outputs": [], "source": [ "encoded_values = encoder.fit_transform(data[['occupation','race']])\n", "new_columns = encoder.get_feature_names_out(['occupation' ,'race'])" ] }, { "cell_type": "code", "execution_count": 42, "id": "4222de0e-def7-4a80-8593-e32fe771eae2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['occupation_Adm-clerical', 'occupation_Armed-Forces',\n", " 'occupation_Craft-repair', 'occupation_Exec-managerial',\n", " 'occupation_Farming-fishing', 'occupation_Handlers-cleaners',\n", " 'occupation_Machine-op-inspct', 'occupation_Other-service',\n", " 'occupation_Priv-house-serv', 'occupation_Prof-specialty',\n", " 'occupation_Protective-serv', 'occupation_Sales',\n", " 'occupation_Tech-support', 'occupation_Transport-moving',\n", " 'occupation_nan', 'race_Amer-Indian-Eskimo',\n", " 'race_Asian-Pac-Islander', 'race_Black', 'race_Other',\n", " 'race_White'], dtype=object)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_columns" ] }, { "cell_type": "code", "execution_count": 43, "id": "c4d8f20e-8336-4f31-baec-43c4844ac065", "metadata": {}, "outputs": [], "source": [ "a = pd.DataFrame(encoded_values,columns = new_columns,index = data.index)\n", "data_final = pd.concat(\n", " [data.drop(columns = ['occupation','race',]),a],\n", " axis = 1\n", ")" ] }, { "cell_type": "code", "execution_count": 44, "id": "c0470a35-80c8-4784-96b3-e93136387f4b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ageworkclassfnlwgteducationeducation-nummarital-statusrelationshipsexcapitalgaincapitalloss...occupation_Protective-servoccupation_Salesoccupation_Tech-supportoccupation_Transport-movingoccupation_nanrace_Amer-Indian-Eskimorace_Asian-Pac-Islanderrace_Blackrace_Otherrace_White
02State-gov77516Bachelors13Never-marriedNot-in-familyMale10...0.00.00.00.00.00.00.00.00.01.0
13Self-emp-not-inc83311Bachelors13Married-civ-spouseHusbandMale00...0.00.00.00.00.00.00.00.00.01.0
22Private215646HS-grad9DivorcedNot-in-familyMale00...0.00.00.00.00.00.00.00.00.01.0
33Private23472111th7Married-civ-spouseHusbandMale00...0.00.00.00.00.00.00.01.00.00.0
41Private338409Bachelors13Married-civ-spouseWifeFemale00...0.00.00.00.00.00.00.01.00.00.0
..................................................................
488372Private215419Bachelors13DivorcedNot-in-familyFemale00...0.00.00.00.00.00.00.00.00.01.0
488384NaN321403HS-grad9WidowedOther-relativeMale00...0.00.00.00.01.00.00.01.00.00.0
488392Private374983Bachelors13Married-civ-spouseHusbandMale00...0.00.00.00.00.00.00.00.00.01.0
488402Private83891Bachelors13DivorcedOwn-childMale20...0.00.00.00.00.00.01.00.00.00.0
488411Self-emp-inc182148Bachelors13Married-civ-spouseHusbandMale00...0.00.00.00.00.00.00.00.00.01.0
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48842 rows × 33 columns

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" ], "text/plain": [ " age workclass fnlwgt education education-num \\\n", "0 2 State-gov 77516 Bachelors 13 \n", "1 3 Self-emp-not-inc 83311 Bachelors 13 \n", "2 2 Private 215646 HS-grad 9 \n", "3 3 Private 234721 11th 7 \n", "4 1 Private 338409 Bachelors 13 \n", "... .. ... ... ... ... \n", "48837 2 Private 215419 Bachelors 13 \n", "48838 4 NaN 321403 HS-grad 9 \n", "48839 2 Private 374983 Bachelors 13 \n", "48840 2 Private 83891 Bachelors 13 \n", "48841 1 Self-emp-inc 182148 Bachelors 13 \n", "\n", " marital-status relationship sex capitalgain capitalloss \\\n", "0 Never-married Not-in-family Male 1 0 \n", "1 Married-civ-spouse Husband Male 0 0 \n", "2 Divorced Not-in-family Male 0 0 \n", "3 Married-civ-spouse Husband Male 0 0 \n", "4 Married-civ-spouse Wife Female 0 0 \n", "... ... ... ... ... ... \n", "48837 Divorced Not-in-family Female 0 0 \n", "48838 Widowed Other-relative Male 0 0 \n", "48839 Married-civ-spouse Husband Male 0 0 \n", "48840 Divorced Own-child Male 2 0 \n", "48841 Married-civ-spouse Husband Male 0 0 \n", "\n", " ... occupation_Protective-serv occupation_Sales \\\n", "0 ... 0.0 0.0 \n", "1 ... 0.0 0.0 \n", "2 ... 0.0 0.0 \n", "3 ... 0.0 0.0 \n", "4 ... 0.0 0.0 \n", "... ... ... ... \n", "48837 ... 0.0 0.0 \n", "48838 ... 0.0 0.0 \n", "48839 ... 0.0 0.0 \n", "48840 ... 0.0 0.0 \n", "48841 ... 0.0 0.0 \n", "\n", " occupation_Tech-support occupation_Transport-moving occupation_nan \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "... ... ... ... \n", "48837 0.0 0.0 0.0 \n", "48838 0.0 0.0 1.0 \n", "48839 0.0 0.0 0.0 \n", "48840 0.0 0.0 0.0 \n", "48841 0.0 0.0 0.0 \n", "\n", " race_Amer-Indian-Eskimo race_Asian-Pac-Islander race_Black \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 \n", "... ... ... ... \n", "48837 0.0 0.0 0.0 \n", "48838 0.0 0.0 1.0 \n", "48839 0.0 0.0 0.0 \n", "48840 0.0 1.0 0.0 \n", "48841 0.0 0.0 0.0 \n", "\n", " race_Other race_White \n", "0 0.0 1.0 \n", "1 0.0 1.0 \n", "2 0.0 1.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "... ... ... \n", "48837 0.0 1.0 \n", "48838 0.0 0.0 \n", "48839 0.0 1.0 \n", "48840 0.0 0.0 \n", "48841 0.0 1.0 \n", "\n", "[48842 rows x 33 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_final" ] }, { "cell_type": "code", "execution_count": 45, "id": "1d40ff5e-3e51-4311-91e8-e87471e6b7c2", "metadata": {}, "outputs": [], "source": [ "X,y = load_breast_cancer(return_X_y = True )\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "knn = KNeighborsClassifier()#we can fix no of neighbour" ] }, { "cell_type": "code", "execution_count": 46, "id": "1a5c7e46-4280-4279-86a4-d821f0165868", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier()
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" ], "text/plain": [ "KNeighborsClassifier()" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(X_train_scaled,y_train)" ] }, { "cell_type": "code", "execution_count": 47, "id": "43e4316a-c2da-495e-81df-f14e565ed184", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9912280701754386" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.score(X_test_scaled,y_test)" ] }, { "cell_type": "code", "execution_count": 48, "id": "168d8322-c73a-461f-9282-63ecdb5a3ce3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n", " 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n", " 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n", " 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n", " 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n", " 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n", " 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n", " 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n", " 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n", " 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n", " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n", " 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n", " 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n", " 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n", " 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 49, "id": "6425403a-9043-4467-8d20-86cbd56fbbbe", "metadata": {}, "outputs": [], "source": [ "first_instant = X_test_scaled[5]#it select the row" ] }, { "cell_type": "code", "execution_count": 50, "id": "09c2d8a8-4e4f-4ea5-b419-288579d1d620", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.88147194, 0.64279025, 0.82091807, 0.79388625, 0.0505427 ,\n", " -0.26115932, 0.03926009, 0.43200918, 0.2213698 , -0.98478958,\n", " 0.04949836, -0.86236309, -0.03134759, 0.12944226, -0.84413082,\n", " -0.74524647, -0.44433388, -0.3267797 , -1.10544537, -0.88425805,\n", " 1.15339344, 0.61426841, 1.03691473, 1.05267897, 1.02465886,\n", " 0.0269279 , 0.59371507, 1.30331537, 0.54821227, -0.43732921])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_instant" ] }, { "cell_type": "code", "execution_count": 51, "id": "4e2cd46c-5014-4c48-8cdb-9313493962e3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0])" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.predict([first_instant])" ] }, { "cell_type": "code", "execution_count": 52, "id": "e6bb9365-600d-4de6-b97e-b5ed4be8a43a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b\n" ] } ], "source": [ "if knn.predict([first_instant]) == np.array([1]):\n", " print('a')\n", "else:\n", " print('b')" ] }, { "cell_type": "code", "execution_count": 53, "id": "c219f6f3-d516-4d78-bc44-3182929e9f00", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LinearRegression()
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" ], "text/plain": [ "LinearRegression()" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X,y = fetch_california_housing(return_X_y = True)\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "reg = LinearRegression()\n", "reg.fit(X_train_scaled ,y_train)" ] }, { "cell_type": "code", "execution_count": 54, "id": "88edf361-76e0-418b-9ff2-7208514d7da1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5973663003866243" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg.score(X_test_scaled,y_test)# it is R^2 - score" ] }, { "cell_type": "code", "execution_count": 55, "id": "0cb23902-da3f-4418-a258-427e07c23cb6", "metadata": {}, "outputs": [], "source": [ "first_instant = X_test_scaled[1]" ] }, { "cell_type": "code", "execution_count": 56, "id": "4a2845c7-2fb1-4274-9089-f775d02ed4a0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.00918536, 0.03052176, 0.39131574, -0.11732794, 0.04278488,\n", " -0.00633355, 0.63925073, -1.21074496])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_instant " ] }, { "cell_type": "code", "execution_count": 57, "id": "dae735eb-8d09-4f85-9740-3cd65b51ef4a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3.25831562])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg.predict([first_instant])" ] }, { "cell_type": "code", "execution_count": 58, "id": "3b96ca31-8fa6-4941-8af0-543a42280df4", "metadata": {}, "outputs": [], "source": [ "# now let see our prediction \n", "y_predicted = reg.predict(X_test_scaled)" ] }, { "cell_type": "code", "execution_count": 59, "id": "1cbfb8bc-a6af-456c-bead-22ce905f7732", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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actualpredicted
02.6312.206037
12.8153.258316
22.0801.692337
31.6861.690921
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52.9842.167313
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\n", "
" ], "text/plain": [ " actual predicted\n", "0 2.631 2.206037\n", "1 2.815 3.258316\n", "2 2.080 1.692337\n", "3 1.686 1.690921\n", "4 1.125 5.507162\n", "5 2.984 2.167313\n", "6 2.422 1.827546\n", "7 2.203 2.075481\n", "8 1.130 2.045415\n", "9 2.966 2.534190" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comparision = pd.DataFrame({\n", " \"actual\" :y_test[:10],\n", " \"predicted\" :y_predicted[:10]\n", "})\n", "comparision\n" ] }, { "cell_type": "code", "execution_count": 60, "id": "8cbb659f-fe17-433a-906f-f951ef3dc6a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5932844429271913\n" ] } ], "source": [ "#some other powerful model\n", "from sklearn.tree import DecisionTreeRegressor\n", "tree = DecisionTreeRegressor()\n", "tree.fit(X_train_scaled, y_train)\n", "print(tree.score(X_test_scaled, y_test))\n" ] }, { "cell_type": "code", "execution_count": 61, "id": "d90eed55-0c12-4c96-9e7e-49dd97b2d263", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8051306342606193\n" ] }, { "data": { "text/plain": [ "(4128, 8)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "rf = RandomForestRegressor()\n", "rf.fit(X_train_scaled, y_train)\n", "print(rf.score(X_test_scaled, y_test))\n", "X_test_scaled.shape" ] }, { "cell_type": "code", "execution_count": 62, "id": "91a85a6b-3019-49d6-816c-96e09568da9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7794645112152417\n" ] } ], "source": [ "from sklearn.ensemble import GradientBoostingRegressor\n", "gb = GradientBoostingRegressor()\n", "gb.fit(X_train_scaled, y_train)\n", "print(gb.score(X_test_scaled, y_test))\n" ] }, { "cell_type": "code", "execution_count": 63, "id": "6c4e94d6-cc87-4a3f-98fd-8acfd25346f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6667682956437215\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsRegressor\n", "gb = KNeighborsRegressor()\n", "gb.fit(X_train_scaled, y_train)\n", "print(gb.score(X_test_scaled, y_test))\n" ] }, { "cell_type": "code", "execution_count": 64, "id": "86787ef1-d4da-4088-9464-7e219baa56ca", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(y_test, y_predicted, alpha=0.5)\n", "plt.xlabel(\"Actual Prices\")\n", "plt.ylabel(\"Predicted Prices\")\n", "plt.title(\"Actual vs Predicted\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a1ca5928-f6c5-4901-9b51-28a3cba88571", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 65, "id": "d03677f8-6a7d-44c4-8709-0b66a520f4ef", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "X, _ = make_blobs(n_samples=5000, random_state=13, centers=4)\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X)\n", "plt.scatter(X_scaled[:, 0], X_scaled[:, 1])\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 66, "id": "edae6b9e-3596-4bfe-8260-7dbb69364a7b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KMeans(n_clusters=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "KMeans(n_clusters=4)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kmeans = KMeans(n_clusters=4)\n", "kmeans.fit(X_scaled)" ] }, { "cell_type": "code", "execution_count": 67, "id": "ce28d15f-8c4c-46af-aeac-b9dd21e3505b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X_scaled[:, 0], X_scaled[:, 1],c=kmeans.labels_)" ] }, { "cell_type": "code", "execution_count": 68, "id": "c30ad9a1-4fef-4572-af92-7f0675234b3c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " X, _ = make_moons(n_samples=5000, random_state=110 ,noise=0.05)\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X)\n", "dbscan = DBSCAN(eps = 0.3)\n", "dbscan.fit(X_scaled)\n", "plt.scatter(X_scaled[:, 0], X_scaled[:, 1],c =dbscan.labels_ )" ] }, { "cell_type": "code", "execution_count": 72, "id": "6ffd6e9d-368d-4b6e-947f-37e74e740856", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Clear the OpenML cache before fetching the dataset\n", "import os\n", "import shutil\n", "from sklearn.datasets import get_data_home\n", "\n", "# Clear the cache\n", "cache_dir = os.path.join(get_data_home(), 'openml')\n", "if os.path.exists(cache_dir):\n", " shutil.rmtree(cache_dir)\n", "\n", "# Now fetch the dataset with version specified\n", "X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)\n", "\n", "# Split the data\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)" ] }, { "cell_type": "code", "execution_count": 73, "id": "e48eb6b5-a46d-4b37-8a2e-acb626cee06d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]])" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X" ] }, { "cell_type": "code", "execution_count": 74, "id": "344c2412-7e4c-445e-8626-28da9ba7968b", "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components= 20 )\n", "X_train_reduced = pca.fit_transform(X_train)\n", "X_test_reduced = pca.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 75, "id": "545aa7b8-34b8-488a-8f8b-06b53328314c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(49000, 20)" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_reduced.shape" ] }, { "cell_type": "code", "execution_count": 76, "id": "14971668-1042-4965-95c8-17d6cce4c93d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(49000, 784)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": 77, "id": "d645bd72-bc66-4b91-ac8a-4d9654e1c909", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 10 iteration(s) (status=1):\n", "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n", "\n", "Increase the number of iterations to improve the convergence (max_iter=10).\n", "You might also want to scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/plain": [ "0.8845238095238095" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = LogisticRegression(max_iter = 10)\n", "clf.fit(X_train,y_train)\n", "clf.score(X_test,y_test)" ] }, { "cell_type": "code", "execution_count": 78, "id": "6ab6d9b0-5f18-4bc5-ba8e-5a28a962cfda", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 10 iteration(s) (status=1):\n", "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n", "\n", "Increase the number of iterations to improve the convergence (max_iter=10).\n", "You might also want to scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/plain": [ "0.8650476190476191" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = LogisticRegression(max_iter = 10)\n", "clf.fit(X_train_reduced,y_train)\n", "clf.score(X_test_reduced,y_test)" ] }, { "cell_type": "code", "execution_count": 79, "id": "be8a4fc2-40fc-4ca2-9900-7b85f2e6c013", "metadata": {}, "outputs": [], "source": [ "y_pred = clf.predict(X_test_reduced)" ] }, { "cell_type": "code", "execution_count": 81, "id": "92d5ee0f-6be5-406e-bd39-e47babc2e72a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 20 predictions: ['8' '4' '5' '7' '7' '0' '6' '2' '7' '4' '3' '9' '7' '8' '2' '5' '9' '1'\n", " '7' '8']\n" ] } ], "source": [ "print(\"First 20 predictions:\", y_pred[:20])\n" ] }, { "cell_type": "code", "execution_count": 82, "id": "1286afb6-d479-4d4c-86ef-bcaf4e9e7eb1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.8650476190476191\n" ] } ], "source": [ "\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(\"Accuracy:\", accuracy)" ] }, { "cell_type": "code", "execution_count": null, "id": "171008fa-fafb-4d21-8995-787e5785cfcb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 83, "id": "0f0a743b-c817-4607-a4a4-ba56c8559cca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9824561403508771" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X,y = load_breast_cancer(return_X_y = True )\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)\n", "scaler = StandardScaler()\n", "X_train_scaler = scaler.fit_transform(X_train)\n", "X_test_scaler = scaler.transform(X_test)\n", "knn = KNeighborsClassifier()\n", "knn.fit(X_train_scaler,y_train)\n", "knn.score(X_test_scaler,y_test)" ] }, { "cell_type": "code", "execution_count": 84, "id": "2078c55e-b2c8-4d84-8839-0acb7f22945e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9824561403508771" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = knn.predict(X_test_scaler)\n", "accuracy_score(y_pred,y_test)\n" ] }, { "cell_type": "code", "execution_count": 85, "id": "1f42a4b3-eaff-4814-8dce-8f157e5ce67d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "precision_score(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 86, "id": "42fc87a8-4fd7-46cd-9946-29daa2dc40b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9736842105263158" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recall_score(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 87, "id": "3c09be53-4a00-4681-8433-878cd36bce17", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9866666666666667" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1_score(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 88, "id": "0ef957e8-506c-4978-ac37-741325636851", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LinearRegression()
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" ], "text/plain": [ "LinearRegression()" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X,y = fetch_california_housing(return_X_y = True)\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "reg = LinearRegression()\n", "reg.fit(X_train_scaled ,y_train)" ] }, { "cell_type": "code", "execution_count": 89, "id": "8a33dbef-8367-44e3-8084-df6b78f46761", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6086300784979082" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg.score(X_train_scaled,y_train)\n" ] }, { "cell_type": "code", "execution_count": 90, "id": "ac1d341a-b554-44ea-a115-7162ab241860", "metadata": {}, "outputs": [], "source": [ "y_pred = reg.predict(X_test_scaled)" ] }, { "cell_type": "code", "execution_count": 91, "id": "0d5382d6-35f1-4de5-a912-177282a8a0b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.35700751496054683" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_score(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 92, "id": "a98824ff-86b8-4356-9251-247a6e3c997d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5311995389312596" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_absolute_error(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 93, "id": "3b06f908-b9e1-4e37-bc89-0cb98f8abd22", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7208192710944857" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "root_mean_squared_error(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 94, "id": "8d82d404-17f2-4e8e-80ae-70ac105f4b00", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5195804215811857" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_squared_error(y_pred,y_test)" ] }, { "cell_type": "code", "execution_count": 95, "id": "53220dcc-c289-4864-81cb-fd40cf5932ad", "metadata": {}, "outputs": [], "source": [ "X,y = load_breast_cancer(return_X_y=True)\n", "scaler = StandardScaler()\n", "knn = KNeighborsClassifier()\n", "X_scaled = scaler.fit_transform(X)\n", "scored = cross_val_score(knn,X_scaled,y,cv=5)\n", "score_on_precision = cross_val_score(knn ,X_scaled,y,scoring= 'precision',cv=5)\n", " " ] }, { "cell_type": "code", "execution_count": 96, "id": "08561cc0-40f6-4ca1-b62f-4581402bc170", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.96491228, 0.95614035, 0.98245614, 0.95614035, 0.96460177])" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scored" ] }, { "cell_type": "code", "execution_count": 97, "id": "6f4be3d4-9df2-465e-8e33-45f2bada3e80", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.9648501785437045)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(scored)" ] }, { "cell_type": "code", "execution_count": 98, "id": "5a5b6d90-248e-4700-8b93-93984ae5bfa2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.97183099, 0.94594595, 0.97297297, 0.93506494, 0.98550725])" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score_on_precision" ] }, { "cell_type": "code", "execution_count": 99, "id": "3859e736-63ea-4b22-875d-cf539ade7adb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.9622644172552318)" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(score_on_precision)" ] }, { "cell_type": "code", "execution_count": 100, "id": "373f96ac-c8d2-4c6b-a50f-7377d82c02b3", "metadata": {}, "outputs": [], "source": [ "X,y = load_breast_cancer(return_X_y=True)\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=0)\n", "rfc = RandomForestClassifier(n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 101, "id": "ed037f67-311c-44bc-a818-ff589cc02d97", "metadata": {}, "outputs": [], "source": [ "parameter_grid = {\n", " 'n_estimators' : [50,100,500],\n", " 'max_depth': [None , 5,10],\n", " 'min_samples_split' : [2,5,20]\n", " \n", "\n", "}" ] }, { "cell_type": "code", "execution_count": 102, "id": "3d99f2d2-6a24-4617-9ee6-bf9a19f37b96", "metadata": {}, "outputs": [], "source": [ "grid = GridSearchCV(rfc ,parameter_grid,cv = 3)" ] }, { "cell_type": "code", "execution_count": 103, "id": "047ec4f8-0443-43f2-8056-4019c48c68ca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=3, estimator=RandomForestClassifier(n_jobs=-1),\n",
       "             param_grid={'max_depth': [None, 5, 10],\n",
       "                         'min_samples_split': [2, 5, 20],\n",
       "                         'n_estimators': [50, 100, 500]})
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" ], "text/plain": [ "GridSearchCV(cv=3, estimator=RandomForestClassifier(n_jobs=-1),\n", " param_grid={'max_depth': [None, 5, 10],\n", " 'min_samples_split': [2, 5, 20],\n", " 'n_estimators': [50, 100, 500]})" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.fit(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 104, "id": "1afdd56b-9adb-4c05-8d9c-1986afd60aee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'max_depth': 10, 'min_samples_split': 5, 'n_estimators': 100}" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.best_params_" ] }, { "cell_type": "code", "execution_count": 105, "id": "bea2d890-1648-4286-a109-dce75af344da", "metadata": {}, "outputs": [], "source": [ "a = grid.best_estimator_" ] }, { "cell_type": "code", "execution_count": 106, "id": "24ebece1-7978-4e43-8732-55a15d0216e8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
RandomForestClassifier(max_depth=10, min_samples_split=5, n_jobs=-1)
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" ], "text/plain": [ "RandomForestClassifier(max_depth=10, min_samples_split=5, n_jobs=-1)" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 107, "id": "92057f0f-7e5c-497a-a94b-ef414600a07e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9649122807017544" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.score(X_test,y_test)" ] }, { "cell_type": "code", "execution_count": 108, "id": "d5ae106b-d214-4496-93e9-e7048424f85b", "metadata": {}, "outputs": [], "source": [ "X,y = load_breast_cancer(return_X_y=True)\n", "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=0)\n", "pipe = Pipeline([\n", " ('scale' , StandardScaler()),\n", " ('pca',PCA()),\n", " ('forest',RandomForestClassifier())\n", "])" ] }, { "cell_type": "code", "execution_count": 109, "id": "af7d1af6-4fed-473f-82a8-6ac13a4bb9a7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('scale', StandardScaler()), ('pca', PCA()),\n",
       "                ('forest', RandomForestClassifier())])
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" ], "text/plain": [ "Pipeline(steps=[('scale', StandardScaler()), ('pca', PCA()),\n", " ('forest', RandomForestClassifier())])" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe.fit(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 110, "id": "7ee300fa-517d-458b-b808-0090c35fdb43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9035087719298246" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe.score(X_test,y_test)" ] }, { "cell_type": "code", "execution_count": null, "id": "19cec278-7c45-4caa-8300-d05377ece4d4", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6b0be717-0997-44f0-a0c0-c31a6a87c787", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }