Predict the average spend of customers for the next 3 months in a regression problem.

The task involves predicting customer spending for the next 3 months based on advertisement details. The dataset includes training and testing data, with features outlined in the data dictionary.

1. Import Libraries:

   • Import necessary libraries for data analysis and model building.

2. Load Data:

   • Load training and testing data along with the data dictionary.

3. Data Exploration and Preprocessing:

   • Treat NULL values and handle outliers.
   • Explore gender distribution and perform basic data analysis.

4. Feature Engineering:

   • Log-transform right-skewed columns.
   • Check and address collinearity issues.

5. Data Preprocessing:

   • Label encode categorical variables and standardize numerical features.

6. Train-Test Split:

   • Split the data into training and validation sets.

7. Model Building:

   • Utilize Linear Regression, Random Forest, and CatBoost models for prediction.

8. Model Evaluation:

   • Assess model performance using RMSLE scores.

9. Making Predictions:

   • Use trained models to predict customer spending on the test data.