Telco Customer Churn Prediction in Python

We need to load dataset from URL, compute churn rate (percentage of customers with Churn = Yes?). Provide code that loads, shows shape, maybe head, then compute churn rate. According to instructions, first step: load and inspect: shape, columns, head. Also compute churn rate. Provide code block with loading, show shape, head, and churn rate. End with a final expression that displays churn rate perhaps. Let's produce code.

We need to decide if we have enough to answer original goal: "load the telco churn dataset and show churn rate". We have loaded dataset, computed churn_rate = 26.536987079369588 (percentage). So answer: churn rate about 26.54%. Provide final answer with the numeric. No code. Path A.

User wants to encode categorical variables and show class balance. Currently df has many columns; we need to identify categorical columns. We'll encode using one-hot (pd.get_dummies) or label encoding for binary columns. Then show class balance (counts percentages) after encoding perhaps not needed. Provide code to separate target and features, encode features, and display class distribution.

We need to ensure we don't encode target column. We'll create X = df.drop('Churn', axis=1), y = df['Churn']. Then use get_dummies on X, with drop_first maybe. Then show class balance: y.value_counts(normalize=True)*100. Provide code.

Goal: encode categorical variables and show class balance. The user already executed encoding and displayed shape, and computed class balance (percentage). So we have both encoded matrix shape and class balance percentages. That's sufficient. Need to provide final answer: mention the shape after encoding and the class distribution percentages. No code. Provide final answer with comment <!--final answer-->.

We need to train a RandomForest classifier on the encoded data (X_encoded) and target y. Need to split into train/test, perhaps using train_test_split, then fit RandomForestClassifier, evaluate accuracy on test set, perhaps also show classification report.

Follow notebook steps: current variables: X_encoded, y exist. We should avoid ID column like customerID. In encoding we dropped Churn target but kept customerID, which is an identifier and likely not predictive. Should drop it. In X we had all columns except Churn, including customerID. Should remove that before encoding. But we already encoded with get_dummies; the customerID column is a string, so it got turned into many dummy columns (maybe many unique values). That's undesirable. We need to correct: encode after dropping ID column to avoid high dimension. So we can redo encoding: start over: X = df.drop(columns=['Churn', 'customerID']). Then encode categorical columns. Since we already have X_encoded variable, we might create a new X_enc2 after dropping ID columns.

Simplify: create X_clean = df.drop(columns=['Churn', 'customerID']). Then get dummies. Then split. Train RandomForestClassifier with default parameters (or set n_estimators=100, random_state=42). Compute accuracy and maybe show classification report.

Provide code step: do preprocessing and training, then output accuracy.

Since we are to iterate one step at a time, propose to create cleaned encoded features and split, train model, compute accuracy, and display. We can produce code that does all that in one cell (creating X_clean_encoded, train_test_split, model, accuracy). That's okay as one step.

We'll output accuracy (float) as final expression.

Let's write code accordingly.

We have output from cell-5: accuracy 79.418... So we have trained a RandomForest, predicted, and computed accuracy. That answers original goal: train a random forest classifier and show accuracy. Provide final answer with the accuracy. No code needed. Include <!--final answer-->.

We need to plot top 10 most important features from the RandomForest trained in cell-5. Use seaborn/matplotlib. We have rf feature_importances_, and X_enc columns list. Need to get importances, sort, select top 10, and plot bar chart.

We need to produce code block that creates the plot, using matplotlib/seaborn, display the chart. Provide brief explanation? The instruction: respond concisely, include code block. Since we have data, we can directly compute.

We should ensure that we use variable names from previous cells: rf, X_enc. Also need to import seaborn, matplotlib.pyplot.

We'll create DataFrame of feature importance, sort, get top 10, plot horizontal bar.

We will output the plot and also maybe display the DataFrame of top features. Provide code block with final expression maybe the plot display returns the axes object.

Let's write code.

We have a user goal: "plot the top 10 most important features for churn prediction". The notebook already has a cell 6 that created a bar plot and displayed a table top10 with features and importances. The output shows the figure and a table printed (the top10 DataFrame). So the goal is achieved: plotted top 10 and displayed values. The instruction says: For ranking tasks, do not choose Path A from chart-only output; first produce a value table/list via Path B. But here we have both chart and the printed table top10. The instructions: "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first and print the values used in the chart. For ranking tasks (for example top features...), do not choose Path A from chart-only output; first produce a value table/list via Path B." However we already have a table printed (the output shows the DataFrame). So the underlying values are present. Therefore Path A is permissible. We should provide final answer summarizing the insights: top features and that we plotted them. So we give final answer with no code.