Implemented Random Forest and Data Cleaning Techniques

📅 2023-02-25 — Session: Implemented Random Forest and Data Cleaning Techniques

🕒 20:10–21:40
🏷️ Labels: Python, Data Cleaning, Random Forest, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to implement a Random Forest Regressor using scikit-learn in Python and address data cleaning issues in a property dataset.

Key Activities:

• Implemented a Random Forest Regressor in Python using scikit-learn, focusing on data loading, preprocessing, model fitting, and making predictions.
• Fixed DataFrame modification warnings by creating copies of data and handling missing values, specifically computing the price per square meter.
• Investigated and cleaned NaN values in the dataset, ensuring accurate computation of price_m2 values.
• Validated and converted data types in the price_m2 column to ensure they contain valid numeric values.
• Addressed KeyError in label encoding by updating the LabelEncoder with new labels found in the test data.

Achievements:

• Successfully implemented a Random Forest Regressor and resolved data cleaning issues, improving the dataset’s integrity for analysis.
• Ensured the robustness of the data preprocessing pipeline by handling potential errors and warnings.

Pending Tasks:

• Further testing and validation of the Random Forest model’s performance on the cleaned dataset.
• Documentation of the data preprocessing steps and model implementation for future reference.