Implemented Random Forest Regressor and Data Cleaning Techniques

• Day: 2023-02-25
• Time: 20:10 to 21:40
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Cleaning, Random Forest, Machine Learning, Data Analysis

Description

Session Goal

The primary goal of this session was to implement a random forest regressor using scikit-learn in Python and to address various data cleaning challenges in a property dataset.

Key Activities

• Implemented a random forest regressor using scikit-learn, including data loading, preprocessing, model fitting, and making predictions.
• Addressed DataFrame modification warnings by creating a copy and performing calculations to avoid altering the original data.
• Investigated and handled NaN values in the price and surface_total columns to ensure accurate computation of price_m2 values.
• Analyzed NaN values in the ‘price_m2’ column post-groupby operation to compute mean prices per square meter.
• Validated and converted the price_m2 column to ensure it contains valid numeric values, converting invalid entries to NaN for accurate mean calculation.
• Solved a KeyError in label encoding by adding new labels to the encoder’s classes before transforming test data.

Achievements

• Successfully implemented a random forest regressor and addressed data cleaning issues, ensuring accurate data manipulation and model predictions.
• Developed a comprehensive README file in markdown for a Python repository implementing a follow-unfollow scheme with Tweepy.

Pending Tasks

• Further validation of the random forest regressor’s performance on additional datasets.
• Continuous monitoring and adjustment of data preprocessing steps to handle new data anomalies.

Evidence

• source_file=2023-02-25.sessions.jsonl, line_number=1, event_count=0, session_id=731ee0d17521f27f7c1f93eb6e381658a15517759c0bda357f4dbbf2ba90a5b4
• event_ids: []