Telco Customer Churn Prediction in Python

The user wants to load the Telco Churn dataset from a specific URL and calculate the churn rate.

Plan:

1. Load the dataset using pandas.
2. Inspect the data (head, shape, columns) as per notebook rules for the first load.
3. Calculate the churn rate.

Wait, the notebook rules say: "When a dataset is not yet loaded in current notebook context, first load and inspect only: show shape, columns, and head()." and "Do not combine first-load data inspection with plotting/modeling in the same response."

The user specifically asked for the churn rate. However, the safety/style guidelines for the notebook environment prioritize a clean "load and inspect" phase first.

Let's start by loading the data and inspecting it. Once we see the column names and types, we can calculate the churn rate in the next step.

Step 1: Load data and inspect.

The user wants to load the Telco churn dataset and show the churn rate. The dataset has been loaded. df has 7043 rows and 21 columns. The Churn column exists. The churn rate has not been calculated yet.

Path B is necessary to calculate the churn rate.

The user wants to load a specific dataset and show the churn rate.

1. Dataset loaded: df from the provided URL.
2. Churn rate calculated: churn_rate * 100 resulted in 26.536987079369588.

The original goal was to "load the telco churn dataset... and show churn rate". The outputs show:

• The dataset was loaded (Shape: 7043, 21).
• The churn rate is approximately 26.54%.

This is sufficient to answer the user's original goal.

Path A: Final answer.

The user wants to encode categorical variables and show the class balance of the target variable (Churn).

*   Dataset: `df` is already loaded.
*   Target: `Churn` (Yes/No).
*   Categorical columns: `gender`, `Partner`, `Dependents`, `PhoneService`, `MultipleLines`, `InternetService`, `OnlineSecurity`, `OnlineBackup`, `DeviceProtection`, `TechSupport`, `StreamingTV`, `StreamingMovies`, `Contract`, `PaperlessBilling`, `PaymentMethod`.
*   `SeniorCitizen` is already numeric (0/1).
*   `TotalCharges` looks like it might be a string (based on common Telco dataset issues), but I'll focus on the user's request for encoding.
*   Current Churn rate is ~26.54%.

*   Step 1: Visualize/Show class balance (requested).
*   Step 2: Encode categorical variables (requested).

*Decision:* Since the user asked for both "encode" and "show class balance", I should probably show the class balance first or as part of the process. However, "class balance" usually refers to the target variable.

*   Class balance: A simple count plot of the `Churn` column.
*   Encoding: For a churn model, Label Encoding or One-Hot Encoding is typical. Since I need to be iterative, I will start by showing the class balance and then proceed to encoding in the same or next step? The prompt says "encode categorical variables AND show class balance".
*   Wait, I should follow the "one primary objective per response" rule. But "show class balance" is a visualization/inspection step and "encode" is a preprocessing step.
*   Actually, the class balance is already implicitly known from the previous output (`churn_rate`), but a visual representation is better.
*   Let's prioritize the "show class balance" part first or do them together? No, the rules say "Prefer one execution step at a time."
*   Let's start by showing the class balance visually using seaborn, then I'll handle the encoding.

*Actually*, looking at the prompt "encode categorical variables and show class balance", the user wants the data prepared.
If I do a plot first, I satisfy the "show class balance" part.
Then I can encode.

Wait, let's look at the target variable `Churn`. It's currently 'Yes'/'No'.
Encoding categorical variables: I'll use `pd.get_dummies()` for the features and map 'Yes'/'No' to 1/0 for the target.

Let's stick to one step. I will show the class balance plot first.

*Self-Correction:* If I just show the plot, I'm delaying the encoding. If I encode first, I can't show a "Churn" plot easily unless I do it before encoding.
Let's do the class balance plot first to provide the visual insight, then encode in the next turn.

Wait, if I do the plot, I'm just confirming what we already know (26.5%).
Maybe I can do the encoding and then show the balance of the *encoded* target?
Actually, the most helpful first step to "show class balance" is a bar chart.

Let's check if I can do both. The rules say: "Do not provide a full end-to-end pipeline at once... Return at most one Python code block per response."
The "class balance" is a very quick check. I'll start with the plot.

*Refined Plan:*
1. Create a count plot for `Churn`.
2. (Next turn) Encode variables.

Actually, the user might mean they want to *see* the balance as part of the preprocessing.
Let's do the plot.

Wait, I'll use `sns.countplot`.

*Alternative:* I could encode everything and then show the balance? No, plotting the original labels is clearer.

Let's do the class balance visualization first.

*Wait*, if I want to be efficient, I'll just do the plot.

Actually, I'll provide the plot now.

Wait, let me re-read: "encode categorical variables and show class balance".
If I do the plot, I'm halfway there.
If I do encoding, I'm halfway there.
I'll do the plot first.

Actually, looking at the data, `TotalCharges` is often an object (with spaces) in this specific dataset. I should probably check that before encoding, but the user didn't ask for it. I will stick to the request.

Step: Show class balance of `Churn`.

Wait, I can actually do the encoding and then show the balance?
Let's do the plot first.

Actually, I will do the plot for the churn class balance.

*Wait*, maybe the user wants a table of counts? `value_counts()` already did that. A plot is better.

Let's try to be a bit more proactive. I will show the count plot for the churn classes.

The user wants to:

1. Encode categorical variables.
2. Show class balance.

Current state:

• df is loaded.
• Churn rate (26.54%) has been calculated.
• A count plot of Churn has been generated.

What is missing?

• Categorical variables have not been encoded.
• While the class balance was plotted, the actual counts weren't explicitly printed in a table (though the rate was). The prompt asks to "show class balance" and "encode categorical variables".

Plan:

• The "show class balance" part is partially done via the plot, but I should probably provide the numeric counts for completeness.
• The "encode categorical variables" part is completely missing.

I need to encode the categorical variables. Before encoding, I should identify which columns are categorical. The dataset has many columns: gender, Partner, Dependents, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, Churn.

I will use pd.get_dummies for encoding. I'll also include the numeric class balance since the plot is visual.

Wait, the rules say "do not do too much at once" and "one primary objective per response". The original goal was "encode categorical variables and show class balance". The class balance was plotted. I can show the counts and then perform the encoding. Or just do the encoding now since the visualization is already there.

Wait, let's check the "Sufficiency heuristics": "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart." The plot was produced, but numeric counts for class balance were not printed (only the rate).

However, the main part of the user goal "encode categorical variables" hasn't been touched.

I'll provide the numeric class balance and perform the encoding.

Wait, if I do both, is it "too much"? "encode categorical variables and show class balance" is the user's specific request. I'll show the value counts for Churn and then apply encoding.

Actually, let's just do the encoding and the value counts.

Wait, let's look at the columns. customerID should be excluded from encoding (it's an ID). TotalCharges is often read as an object because of empty strings; I should check that or just stick to the categorical encoding requested.

Since I need to encode, I will identify categorical columns (excluding customerID) and use one-hot encoding. I'll also print the class counts.

One code block only.

# Show class balance numerically
print(df['Churn'].value_counts())

# Encode categorical variables
# Identify categorical columns by excluding numeric ones and the ID
cat_cols = df.select_dtypes(include=['object']).columns.drop('customerID')
df_encoded = pd.get_dummies(df, columns=cat_cols, drop_first=True)

df_encoded.head()

But wait, TotalCharges might be an object. If I include it in cat_cols, it will be one-hot encoded, which is wrong. I should check df.info() first? No, I have to make progress. Looking at the head() output provided in the notebook state, TotalCharges is not explicitly shown in the snippet but is in the column list. Usually, TotalCharges in this dataset contains blanks. If I use select_dtypes(include=['object']), TotalCharges will be included if it's not converted.

Better approach for the code block:

1. Print class counts.
2. Define the target Churn and features.
3. Use pd.get_dummies on categorical columns, ensuring we only target the actual categorical descriptors.

Actually, the most robust way is to list categorical columns or use a pattern. Most columns are categorical except tenure, MonthlyCharges, TotalCharges.

Let's just do:

1. Value counts for Churn.
2. One-hot encoding for all object columns except customerID and TotalCharges (which is a known tricky column in this dataset). Or better, just use pd.get_dummies and let the user see the result.

Actually, the prompt says "encode categorical variables". I'll identify categorical columns and apply pd.get_dummies.

# Display numeric class balance
print(df['Churn'].value_counts())

# Identify categorical columns (excluding the ID)
categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
if 'customerID' in categorical_cols:
    categorical_cols.remove('customerID')

# TotalCharges is often an object due to missing values, it should be handled as numeric
if 'TotalCharges' in categorical_cols:
    categorical_cols.remove('TotalCharges')

# Encode categorical variables
df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
df_encoded.head()

Wait, the rule is "minimal runnable Python code block". I'll print the class counts and then perform the encoding.

One more thing: TotalCharges is usually a string in this CSV. If I don't handle it, the model will fail later, but right now the goal is just "encode categorical variables".

Let's refine the code to be very clean.

The user goal is to "encode categorical variables and show class balance".

Current state analysis:

1. Class balance:
   • sns.countplot(x='Churn', data=df) was executed (output-3).
   • df['Churn'].value_counts() was executed (output-4), showing: No: 5174, Yes: 1869.
   • A churn rate of ~26.5% was calculated (output-2).
2. Encoding categorical variables:
   • pd.get_dummies(df, columns=categorical_cols, drop_first=True) was executed (output-4), creating df_encoded.
   • categorical_cols excluded customerID and TotalCharges.

Evaluation:

• Class balance has been shown both visually and numerically.
• Categorical variables have been encoded into df_encoded.
• Both parts of the original user goal have been addressed.

Decision: Path A.

Insights to share:

• The class balance is skewed: 5,174 customers did not churn, while 1,869 did (approximately 26.5% churn rate).
• Categorical features (excluding IDs and TotalCharges) have been successfully transformed into binary dummy variables in the df_encoded dataframe.

Final Answer Construction:

• Address class balance (numeric and visual).
• Address encoding (columns transformed).
• Use <!--final answer-->.

• Current state: Data is loaded (df), class balance is analyzed, and categorical variables are encoded (df_encoded).

  • Goal: Train a Random Forest classifier and show accuracy.

  • Dataset details:

    • Target variable: Churn (categorical: 'Yes'/'No').
    • Features: df_encoded.
    • Known issue: TotalCharges is currently an object (string) because it likely contains blank spaces or needs conversion to float. The previous encoding step skipped it, so it remains in df_encoded as a string/object.
    • Identifier: customerID is present in df_encoded.

  • Step 1: Preprocessing.

    • TotalCharges needs to be converted to numeric.
    • Churn (target) needs to be encoded (e.g., 'Yes' -> 1, 'No' -> 0).
    • customerID must be dropped to avoid leakage/overfitting.
    • Handle missing values (created by pd.to_numeric on TotalCharges).

  • Step 2: Split. Split into training and testing sets.

  • Step 3: Train. Fit a RandomForestClassifier.

  • Step 4: Evaluate. Calculate and show accuracy.

  • Since the prompt asks to "train a random forest classifier and show accuracy", I can't do it all in one go if I follow the "one execution step at a time" rule.

  • However, the very first prerequisite is cleaning TotalCharges and the target variable, as the model cannot handle strings.

  • I should start by preparing the feature matrix $X$ and target vector $y$, handling the TotalCharges conversion and dropping the ID.

  • Target: df_encoded['Churn'] -> map to 0/1.

  • Features: Drop customerID and Churn.

  • TotalCharges: pd.to_numeric(df_encoded['TotalCharges'], errors='coerce') then fill NaNs.

  • Wait, the rule says "Keep one primary objective per response".

  • Objective 1: Prepare data (X, y) and handle TotalCharges.

  • Objective 2: Split and Train/Evaluate.

  Let's start with data preparation for the model.

  • Convert TotalCharges to numeric.
  • Encode Churn target.
  • Define $X$ (dropping ID and target).
  • Handle NaNs in TotalCharges.

  Actually, looking at the instructions: "Prefer one execution step at a time." I'll first prepare the features and target.

• Goal: Train a Random Forest classifier and show accuracy.

  • Latest state: The user attempted to prepare data in cell-5, but it resulted in a KeyError: 'Churn'.

  • Looking at cell-4, df_encoded was created using pd.get_dummies on categorical_cols.

  • In cell-4, categorical_cols included everything in df.select_dtypes(include=['object']) except customerID and TotalCharges. Since Churn is an object column, it was included in categorical_cols and transformed into dummy columns (Churn_Yes, Churn_No - though drop_first=True means only Churn_Yes exists).

  • In cell-5, the user tried to exclude Churn from categorical_cols and re-run the process, but the KeyError: 'Churn' persists. Wait, let's look at the code in cell-5 again.

  • categorical_cols = [col for col in categorical_cols if col not in ['customerID', 'TotalCharges', 'Churn']]

  • df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=True)

  • The df passed to pd.get_dummies is the original df.

  • y = df_encoded['Churn'].map({'Yes': 1, 'No': 0})

  • The error KeyError: 'Churn' happens here. Why?

  • If Churn was NOT in categorical_cols, it should remain in df_encoded.

  • Let's check categorical_cols logic in cell-5: categorical_cols = df.select_dtypes(include=['object']).columns.tolist() -> contains 'Churn'. categorical_cols = [col for col in categorical_cols if col not in ['customerID', 'TotalCharges', 'Churn']] -> 'Churn' is removed. df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=True) -> df_encoded should still have the 'Churn' column.

  • Wait, the KeyError is in cell-5 output. Let me re-read the traceback.

  • The traceback is truncated, but it points to Index.get_loc.

  • Actually, looking at cell-4 output, df_encoded was already created.

  • If the user is running cell-5 in a notebook, it should work unless the df variable itself was modified or if there is some other issue.

  • Actually, looking at the code in cell-5 again: it defines categorical_cols and then df_encoded = pd.get_dummies(df, columns=categorical_cols, drop_first=True). This should keep Churn.

  • Perhaps the error is actually from a previous attempt or a weird state.

  • Wait, let's look at the cell-4 output. df_encoded was created there. If cell-5 failed, I need to fix the data preparation and then proceed to training.

  • The user wants to train a Random Forest and show accuracy.

  • The immediate block is: we can't train until the data is correctly prepared (X and y).

  • The KeyError in cell-5 is strange given the logic, but the logic in cell-5 is technically correct for preserving Churn.

  • Maybe df doesn't have Churn? No, cell-2 and cell-3 use df['Churn'].

  • Could it be that pd.get_dummies behavior is different? If columns is specified, only those columns are encoded. Others are kept.

  • Let's try a cleaner approach to prepare $X$ and $y$ and then split the data.

  • Since we are in Path B (not sufficient), I need to provide the code to fix the preparation and train the model.

  • The user's previous cell failed. I will provide a robust version of the data preparation followed by the Random Forest training and accuracy report.

  • Wait, rules say "one primary objective per response". The objective is "train a random forest classifier and show accuracy".

  • But I can't train it without $X$ and $y$.

  • I will perform the correct data prep AND the training in one go since the user explicitly asked for the model and accuracy, and the previous attempt failed at the prep stage.

  • Wait, "Do not do too much at once." "Keep one primary objective per response."

  • Current state: $X$ and $y$ are not defined due to error.

  • Step 1: Correct data preparation.

  • Step 2: Train and evaluate.

  • However, the user's prompt is "train a random forest classifier and show accuracy".

  • If I only do data prep, I'm not training. If I do both, am I doing too much?

  • Given the context of a "Fix the following code" request previously, the user is stuck.

  • I will prepare the data and train the model. This is one logical objective: "Train and evaluate the model".

  • Refined Plan:

    1. Correct the data prep (handling TotalCharges, encoding categorical variables except Churn, mapping Churn to $y$).
    2. Split data into train/test.
    3. Train RandomForestClassifier.
    4. Print accuracy.

  • Check variables: df exists.

  • Check TotalCharges: it's a string, needs conversion to numeric.

  • Check Churn: needs mapping.

  • Check customerID: must be dropped.

  • Let's double check the KeyError: 'Churn'. If the user ran cell-5 and got KeyError: 'Churn', and the code was y = df_encoded['Churn'].map(...), it means df_encoded did not have the column Churn. This happens if Churn was included in the columns argument of pd.get_dummies. But in cell-5, it was explicitly excluded. This suggests the user might have been running a different version of the code or the notebook state is inconsistent.

  • I will provide a clean, self-contained block that does the prep and training.

  • Wait, the prompt says "If not sufficient, provide brief insights from current outputs and include exactly one minimal runnable Python code block to gather the missing evidence."

  • Actually, the "missing evidence" is the trained model and its accuracy.

  • I'll do the prep and training together.

  • Wait, the prompt says "Do not combine first-load data inspection with plotting/modeling in the same response." I've already loaded and inspected. Now I'm modeling.

  • I'll use train_test_split from sklearn.

  • Constraint Check:

    • No $ for money? No money values mentioned here, but I'll keep it in mind.
    • USD for currency.
    • No chain-of-thought.
    • No headings.
    • One code block.
    • Use RandomForestClassifier.

  • Code details:

    • from sklearn.model_selection import train_test_split
    • from sklearn.ensemble import RandomForestClassifier
    • from sklearn.metrics import accuracy_score
    • df_prep = df.copy()
    • df_prep['TotalCharges'] = pd.to_numeric(df_prep['TotalCharges'], errors='coerce').fillna(0) (or median)
    • X = pd.get_dummies(df_prep.drop(columns=['Churn', 'customerID']), drop_first=True)
    • y = df_prep['Churn'].map({'Yes': 1, 'No': 0})
    • X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    • rf = RandomForestClassifier(random_state=42).fit(X_train, y_train)
    • acc = accuracy_score(y_test, rf.predict(X_test))
    • Print acc.

  • Wait, the KeyError in the previous attempt likely happened because df_encoded was being updated iteratively or incorrectly. I'll ensure a fresh pipeline.

  • Let's refine the insight. The data preparation failed because the Churn column was missing from the encoded dataframe, likely due to being accidentally included in the one-hot encoding process in a previous state. To reach the goal, we need to properly separate the target variable from the features and then fit the Random Forest model.

The user wants me to analyze the latest notebook outputs against the original goal: "train a random forest classifier and show accuracy".

Looking at the notebook state:

• [cell-1] to [cell-5] handle data loading, inspection, and preprocessing (fixing the KeyError: 'Churn').
• [cell-6] trains a RandomForestClassifier using train_test_split, fits it on the training data, and calculates the accuracy_score.
• [output-6] shows the result: 0.7856635911994322.

The original goal was to "train a random forest classifier and show accuracy". The model has been trained and the accuracy has been calculated as approximately 78.57%.

The outputs are sufficient to answer the original goal.

Decision: Path A.

The user wants to plot the top 10 most important features for churn prediction using the previously trained Random Forest classifier (rf).

*   Model: `rf` (RandomForestClassifier) is already trained and fits on `X_train` and `y_train`.
*   Features: `X` (the feature matrix) contains the column names.
*   Accuracy: The model achieved ~78.57% accuracy.

*   `rf.feature_importances_` provides the importance scores.
*   Need to map these scores back to the feature names from `X.columns`.
*   Sort them in descending order and take the top 10.
*   Visualize using a bar chart (Seaborn is the default).

*   Create a DataFrame with feature names and their corresponding importances.
*   Sort and take head(10).
*   Plot using `sns.barplot`.

The user wants to plot the top 10 most important features for churn prediction. Looking at the notebook state:

• cell-6 trained a RandomForestClassifier named rf.
• cell-7 plotted the top 10 features using rf.feature_importances_.
• output-7 shows <Figure size 1000x600 with 1 Axes>, confirming the chart was generated.

However, the "Sufficiency heuristics" in the prompt state: "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart." "For ranking tasks (for example top features, feature importance, top-N categories), do not choose Path A from chart-only output; first produce a value table/list via Path B."

The goal was "plot the top 10 most important features". The plot was created, but the underlying numeric values (the importance scores) are not printed in the notebook outputs. I need to provide the numeric values to complement the visual.

Plan:

1. Acknowledge that the plot was created.
2. Provide a code block to print the actual importance values for the top 10 features to ensure the rankings are precise and transparent.

The user wants to plot the top 10 most important features for churn prediction. Looking at the notebook state:

• Cell 6 trained a RandomForestClassifier.
• Cell 7 plotted the top 10 features using a bar chart.
• Cell 8 displayed the numeric importance scores for the top 10 features.

The output of Cell 8 shows:

1. TotalCharges: 0.190264
2. tenure: 0.175775
3. MonthlyCharges: 0.169087
4. InternetService_Fiber optic: 0.038881
5. PaymentMethod_Electronic check: 0.038856
6. Contract_Two year: 0.030392
7. gender_Male: 0.028152
8. OnlineSecurity_Yes: 0.027985
9. PaperlessBilling_Yes: 0.025842
10. Partner_Yes: 0.023341

The original goal was to "plot the top 10 most important features for churn prediction". The user has both the plot (Cell 7) and the supporting numeric values (Cell 8).

According to the sufficiency heuristics:

• "If the goal is to show/plot/chart/visualize and latest output contains both a valid chart/figure object and explicit numeric/text values supporting the conclusion, choose Path A".

I have:

• Visual (bar chart) from Cell 7.
• Numeric values (DataFrame) from Cell 8.

Conclusion: The original goal is satisfied. I should provide a final answer based on the observed outputs.

Insights:

• The most influential features are TotalCharges, tenure, and MonthlyCharges, which together dominate the importance scores.
• InternetService_Fiber optic and PaymentMethod_Electronic check are the next most significant indicators.
• The combination of billing amounts and customer tenure provides the strongest signal for predicting churn.

Final response should be Path A. No code. No meta phrases. Natural language. Include <!--final answer-->.