README_technical.md

Analysis of the Provided Model Code

The provided code is designed to address several data preprocessing tasks and to build a model for predicting the List_Price. Here’s a detailed breakdown of the steps and methodologies used:

Data Preprocessing Steps:

1. Loading Data:

   • The training and prediction datasets are loaded using pandas.

2. Exploratory Data Analysis (EDA):

   • Information about the data structure is printed using .info().
   • Identification of columns with missing values.
   • Analysis of Count column, including handling non-whole numbers and missing values.

3. Handling Missing Values:

   • Missing Count values are imputed with 0.
   • Non-whole number Count values are set to zero.
   • Missing Category values are predicted using a Decision Tree Classifier trained on existing data.
   • Missing Promo_Price values are predicted using a Random Forest Regressor.

Model Building for List_Price Prediction:

1. Preparing the Data:

   • The features used for the prediction include Retail_Price, Promo_Price, Count, Manufacturer, and Category.
   • One-hot encoding is applied to categorical features (Manufacturer and Category).

2. Splitting Data:

   • The data is split into training and validation sets using train_test_split.

3. Training the Model:

   • A Random Forest Regressor is used to train the model on the training set.
   • Evaluation of the model is performed on the validation set using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2).

4. Saving the Model:

   • The trained model is saved using joblib.

Questions and Answers

1. What data preprocessing steps were taken to handle missing values?

   • Missing Count values were imputed with 0.
   • Missing Category values were predicted using a Decision Tree Classifier.
   • Missing Promo_Price values were predicted using a Random Forest Regressor.

2. How were categorical variables handled in the model?

   • Categorical variables (Manufacturer and Category) were one-hot encoded to convert them into numerical format suitable for the machine learning model.

3. What model was used to predict the List_Price?

   • A Random Forest Regressor was used to predict the List_Price.

4. What metrics were used to evaluate the model's performance?

   • The model's performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2).

5. Were there any techniques used to handle non-whole number Count values?

   • Non-whole number Count values were set to zero during preprocessing.

6. How were missing Category values imputed?

   • Missing Category values were imputed using predictions from a Decision Tree Classifier trained on existing data with known Category values.

7. Is the code modular and reusable for future data predictions?

   • Yes, the code is modular and can be reused. It includes steps for data preprocessing, training, evaluating, and saving the model, which can be adapted for future datasets.

8. What are the potential improvements for the model?

   • Potential improvements could include:
     • Hyperparameter tuning of the Random Forest model using techniques like GridSearchCV.
     • Experimenting with other regression models such as Gradient Boosting or XGBoost.
     • Incorporating additional features that may impact the list price.

If you have any specific questions or need further analysis, feel free to ask!