"C:\\Users\\281879\\Documents\\AI\\week_1_assaingmnets\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 100 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=100).\n",
"You might also want to scale the data as shown in:\n",
"</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight='balanced')</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
"source": "##### No Churn: 0.72 → Model correctly identifies 72% of customers who actually didn't churn. Churn: 0.82 → Model correctly identifies 82% of customers who actually churned this we get from the recall value that is currently needed for the model ,So i given more priority for Recall than the precision.In precision for Churned customers we are getting only 51% accuracy that means we are doing false alarms on our loayal customers but according to business we got a recall of 82% that is we are able to find the customers who is going to churn.",
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"source": "##### precision-whats my prediction and what has happend really ie,if i predicted 100 and i got correct as 51 so my precision is 51%",
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"source": "##### Recall-whats has orginally happend and inside that how much i predicted ie,if there are 100 actually churned and i got a prediction of 82 so my recall is 82%",
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"source": [
"\n",
"##### So in my perspective Recall is more important for this modal because its identifying 82% percent of churned ones\n"
],
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"source": "##### Model is very good in predicting the churned ones so that we can",
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"#### Over all performance of the model is 75% ie,our model correctly predicts 75% of all customers.For non churning customers Precision: 92% - When you predict \"won't churn\", we are almost always right.Recall: 72% - You catch 72% of customers who actually stay.Our Model is giving Strong performance for identifying loyal customers.For Churned customers Precision: 51% - Only half your churn predictions are correct Recall: 82% - our catches 82% of customers who actually churn Good at finding churners, but many false alarms.\n",
"#### Strong ares of our mode ls are\n",
"##### 1).Excellent churn detection (82% recall) - You won't miss many leaving customers\n",
"##### 2). High confidence in loyal customers (92% precision for non-churn)\n",
