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"cells": [
{
"cell_type": "markdown",
"source": [
"Importing all the essential stuff"
],
"metadata": {
"id": "GY0lAyEVygnv"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OT7h2znxcoLp"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "markdown",
"source": [
"Reading and acquiring the dataset"
],
"metadata": {
"id": "9o1rKoKHymIw"
}
},
{
"cell_type": "code",
"source": [
"data=pd.read_csv('HeartDisease.csv')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8V_mP798d15d",
"outputId": "aeebfaf6-d62b-4bc0-eaa6-bd55fdc6333c",
"collapsed": true
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"execution_count": null,
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"text": [
" male age currentSmoker cigsPerDay BPMeds prevalentStroke \\\n",
"0 1 39 0 0 0 0 \n",
"1 0 46 0 0 0 0 \n",
"2 1 48 1 20 0 0 \n",
"3 0 61 1 30 0 0 \n",
"4 0 46 1 23 0 0 \n",
"... ... ... ... ... ... ... \n",
"4132 1 68 0 0 0 0 \n",
"4133 1 50 1 1 0 0 \n",
"4134 1 51 1 43 0 0 \n",
"4135 0 44 1 15 0 0 \n",
"4136 0 52 0 0 0 0 \n",
"\n",
" prevalentHyp diabetes BMI TenYearCHD \n",
"0 0 0 26.97 0 \n",
"1 0 0 28.73 0 \n",
"2 0 0 25.34 0 \n",
"3 1 0 28.58 1 \n",
"4 0 0 23.10 0 \n",
"... ... ... ... ... \n",
"4132 1 0 23.14 1 \n",
"4133 1 0 25.97 1 \n",
"4134 0 0 19.71 0 \n",
"4135 0 0 19.16 0 \n",
"4136 0 0 21.47 0 \n",
"\n",
"[4137 rows x 10 columns]\n",
"[]\n"
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},
{
"cell_type": "markdown",
"source": [
"Getting pandas to understand the data"
],
"metadata": {
"id": "fFJpTqnZy54q"
}
},
{
"cell_type": "code",
"source": [
"df = pd.DataFrame(data)"
],
"metadata": {
"id": "_uL_UiU9eSqS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Determining the Predicting column"
],
"metadata": {
"id": "9L7gSh_6zrP1"
}
},
{
"cell_type": "code",
"source": [
"X=df.drop('TenYearCHD',axis=1)\n",
"y=df['TenYearCHD']"
],
"metadata": {
"id": "2tt1BYjEed0h"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Spliting the dataset into Training and Testing datasets"
],
"metadata": {
"id": "2PvD1TRizwyr"
}
},
{
"cell_type": "code",
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
],
"metadata": {
"id": "H-cuKGVZe2y0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Determining the Model"
],
"metadata": {
"id": "T8SwT0C0z3z6"
}
},
{
"cell_type": "code",
"source": [
"model = LogisticRegression(random_state=42)\n",
"model.fit(X_train, y_train)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 234
},
"id": "ofqGp7tjlu5P",
"outputId": "5071185d-e3aa-4fcb-b0b4-18104e2d313c",
"collapsed": true
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"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: 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"
]
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"Testing the model"
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{
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"source": [
"predictions = model.predict(X_test)\n",
"print(predictions)"
],
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"colab": {
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},
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"id": "Y8UoF5H0kdTc",
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{
"cell_type": "markdown",
"source": [
"Evaluating the model"
],
"metadata": {
"id": "1I7fw8Ce0CKD"
}
},
{
"cell_type": "code",
"source": [
"accuracy = accuracy_score(y_test, predictions)\n",
"print(f'Model Accuracy: {accuracy}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T3J0Mar3kqm3",
"outputId": "8f0f6fda-7b1d-4d42-eafc-f22dea600f39"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model Accuracy: 0.8405797101449275\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Confusion Matrix of the model"
],
"metadata": {
"id": "5QeMo5EV0GYt"
}
},
{
"cell_type": "code",
"source": [
"cm = confusion_matrix(y_test, predictions)"
],
"metadata": {
"id": "I7379CUrlW0n"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(6, 4))\n",
"sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
"plt.xlabel('Predicted')\n",
"plt.ylabel('Actual')\n",
"plt.title('Confusion Matrix')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 410
},
"id": "i5ix5ThYlk_f",
"outputId": "a38c7ab9-0b97-47ab-8ec6-c3746eec5a82"
},
"execution_count": null,
"outputs": [
{
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"data": {
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"<Figure size 600x400 with 1 Axes>"
],
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\n"
},
"metadata": {}
}
]
}
]
} |