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pythondatascience
Commit ddaf5ad5 authored by Pinky Sabu's avatar Pinky Sabu
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add LogicalRegression Approach

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Logical Regression.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "a7699a2b",
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"source": [
"import pandas as pd\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score,confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cd1645cc",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_radius</th>\n",
" <th>mean_texture</th>\n",
" <th>mean_perimeter</th>\n",
" <th>mean_area</th>\n",
" <th>mean_smoothness</th>\n",
" <th>diagnosis</th>\n",
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" <th>0</th>\n",
" <td>17.99</td>\n",
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" <td>122.80</td>\n",
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" <th>2</th>\n",
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" <td>386.1</td>\n",
" <td>0.14250</td>\n",
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" </tr>\n",
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" <th>4</th>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
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"text/plain": [
" mean_radius mean_texture mean_perimeter mean_area mean_smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
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"source": [
"data=pd.read_csv(\"Breast_cancer_data_kaggle.csv\")\n",
"data.head()"
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{
"cell_type": "code",
"execution_count": 8,
"id": "831b3aa1",
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"mean_radius 0\n",
"mean_texture 0\n",
"mean_perimeter 0\n",
"mean_area 0\n",
"mean_smoothness 0\n",
"diagnosis 0\n",
"dtype: int64"
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"source": [
"data.isnull().sum()"
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_radius</th>\n",
" <th>mean_texture</th>\n",
" <th>mean_perimeter</th>\n",
" <th>mean_area</th>\n",
" <th>mean_smoothness</th>\n",
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"text/plain": [
" mean_radius mean_texture mean_perimeter mean_area mean_smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
" diagnosis \n",
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"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoder =LabelEncoder()\n",
"data=data.assign(diagnosis=encoder.fit_transform(data['diagnosis']))\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "baf849e2",
"metadata": {},
"outputs": [],
"source": [
"X= data.loc[:,'mean_radius':]\n",
"y=data['diagnosis']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6211bf28",
"metadata": {},
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_radius</th>\n",
" <th>mean_texture</th>\n",
" <th>mean_perimeter</th>\n",
" <th>mean_area</th>\n",
" <th>mean_smoothness</th>\n",
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" <td>1001.0</td>\n",
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" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
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" <td>1203.0</td>\n",
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"text/plain": [
" mean_radius mean_texture mean_perimeter mean_area mean_smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
" diagnosis \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "385ee841",
"metadata": {},
"outputs": [],
"source": [
"clf=LogisticRegression()\n",
"x_train,x_test,y_train,y_test=train_test_split(X,y ,test_size=.3)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b0f51bf9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/PSabu/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or 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": [
"LogisticRegression()"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(x_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "70855042",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
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" 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0,\n",
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"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred=clf.predict(x_test)\n",
"y_pred"
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},
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"id": "8072530c",
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" [8.87230288e-01, 1.12769712e-01],\n",
" [9.98790981e-01, 1.20901865e-03],\n",
" [9.27730603e-03, 9.90722694e-01],\n",
" [9.42390530e-01, 5.76094699e-02],\n",
" [9.97672597e-01, 2.32740317e-03],\n",
" [2.81933573e-02, 9.71806643e-01],\n",
" [1.65910523e-02, 9.83408948e-01],\n",
" [9.99999948e-01, 5.19271711e-08],\n",
" [1.26379676e-02, 9.87362032e-01],\n",
" [2.50776998e-02, 9.74922300e-01],\n",
" [2.95847359e-03, 9.97041526e-01],\n",
" [5.58595467e-03, 9.94414045e-01],\n",
" [9.53515134e-03, 9.90464849e-01],\n",
" [9.20229917e-01, 7.97700832e-02],\n",
" [4.78613596e-03, 9.95213864e-01],\n",
" [9.96340714e-01, 3.65928552e-03],\n",
" [9.99582479e-01, 4.17520652e-04],\n",
" [9.99440913e-01, 5.59086931e-04],\n",
" [6.89325917e-03, 9.93106741e-01],\n",
" [9.81517478e-01, 1.84825222e-02],\n",
" [1.09067003e-02, 9.89093300e-01],\n",
" [2.63908488e-02, 9.73609151e-01],\n",
" [1.65185253e-02, 9.83481475e-01],\n",
" [7.24373407e-01, 2.75626593e-01],\n",
" [9.55665817e-01, 4.43341829e-02],\n",
" [9.04672314e-01, 9.53276860e-02],\n",
" [2.46990938e-03, 9.97530091e-01],\n",
" [9.99437569e-01, 5.62431407e-04],\n",
" [2.52301917e-02, 9.74769808e-01],\n",
" [9.99997584e-01, 2.41641383e-06],\n",
" [9.99846338e-01, 1.53662312e-04],\n",
" [6.74759482e-03, 9.93252405e-01],\n",
" [9.71237023e-01, 2.87629765e-02],\n",
" [3.23429271e-02, 9.67657073e-01],\n",
" [1.59401812e-02, 9.84059819e-01],\n",
" [9.54145232e-01, 4.58547679e-02],\n",
" [3.82693762e-03, 9.96173062e-01],\n",
" [2.30690778e-03, 9.97693092e-01],\n",
" [9.99937270e-01, 6.27303292e-05],\n",
" [1.30378248e-02, 9.86962175e-01],\n",
" [1.17817686e-02, 9.88218231e-01],\n",
" [4.33370235e-03, 9.95666298e-01],\n",
" [3.37125925e-02, 9.66287408e-01],\n",
" [6.90794736e-03, 9.93092053e-01],\n",
" [3.51004613e-03, 9.96489954e-01],\n",
" [4.08851039e-03, 9.95911490e-01],\n",
" [9.55128087e-01, 4.48719125e-02],\n",
" [7.24861856e-02, 9.27513814e-01],\n",
" [1.88032034e-02, 9.81196797e-01],\n",
" [9.61568184e-02, 9.03843182e-01],\n",
" [1.08714886e-02, 9.89128511e-01],\n",
" [2.18322818e-01, 7.81677182e-01],\n",
" [8.65414084e-03, 9.91345859e-01],\n",
" [2.01507248e-02, 9.79849275e-01],\n",
" [2.68547069e-02, 9.73145293e-01],\n",
" [2.45455007e-02, 9.75454499e-01]])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred=clf.predict_proba(x_test)\n",
"y_pred"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "10d212a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y_pred:[0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 1 0\n",
" 1 1 0 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 0\n",
" 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 0 0 1 0 0 1 1 1\n",
" 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0\n",
" 1 0 1 1 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1]\n",
"y_test:75 0\n",
"253 0\n",
"87 0\n",
"70 0\n",
"153 1\n",
" ..\n",
"291 1\n",
"358 1\n",
"111 1\n",
"121 0\n",
"429 1\n",
"Name: diagnosis, Length: 171, dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"array([[ 65, 0],\n",
" [ 0, 106]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(f\"y_pred:{y_pred}\")\n",
"print(f\"y_test:{y_test}\")\n",
"y_pred=clf.predict(x_test)\n",
"confusion_matrix(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "708999be",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_score(y_test,y_pred)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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