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271.3005221658502,\n \"min\": 0.078,\n \"max\": 768.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 0.47187630208333325,\n 0.3725,\n 768.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 260.1941178528413,\n \"min\": 11.76023154067868,\n \"max\": 768.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 33.240885416666664,\n 29.0,\n 768.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Outcome\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 271.3865920388932,\n \"min\": 0.0,\n \"max\": 768.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.3489583333333333,\n 1.0,\n 0.4769513772427971\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "X=df.drop(columns=['Outcome'])\n", "y=df['Outcome']" ], "metadata": { "id": "FkxjzplitBf1" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LogisticRegression" ], "metadata": { "id": "lgcmWe3mtBi-" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=11)\n" ], "metadata": { "id": "-Wk_vEbKtuen" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "scaler=StandardScaler()\n", "X_train_scaled=scaler.fit_transform(X_train)\n", "X_test_scaled=scaler.transform(X_test)" ], "metadata": { "id": "BDAE9axEtun4" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "model=LogisticRegression(max_iter=2000)" ], "metadata": { "id": "0z_Fz0jEtBmD" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "model.fit(X_train_scaled,y_train)\n", "y_pred=model.predict(X_test_scaled)" ], "metadata": { "id": "5n2e0EVBt0Q-" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix" ], "metadata": { "id": "J1svOkm3t0Tg" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"Accuracy of this model:\",accuracy_score(y_test,y_pred))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sZurgEpWt0WL", "outputId": "6d2c6e37-3221-4193-a4fa-4b03c3742faa" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy of this model: 0.7878787878787878\n" ] } ] }, { "cell_type": "code", "source": [ "print(classification_report(y_test,y_pred))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fDJ-O3kkt0ZE", "outputId": "f36be6a2-7886-4d9a-b848-86cfd861c50f" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.78 0.94 0.85 149\n", " 1 0.82 0.51 0.63 82\n", "\n", " accuracy 0.79 231\n", " macro avg 0.80 0.73 0.74 231\n", "weighted avg 0.79 0.79 0.77 231\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "cm=confusion_matrix(y_test,y_pred)\n", "sns.heatmap(cm,annot=True,fmt='d',cmap='Blues')\n", "\n", "plt.title(\"Confusion matrix\")\n", "plt.xlabel('Predicted Labels')\n", "plt.ylabel(\"Actual Label\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "kXceGsXJt0cU", "outputId": "1009982c-d18f-4491-ca1f-a4204a5ee7de" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "mHq1L9SXt_TQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "nt1da0qzt_V6" }, "execution_count": null, "outputs": [] } ] }