Pathrise_Project This project develops models to predict job placement success and estimated time to placement for Pathrise fellows using their historical data. Objective:

To predict the likelihood of Pathrise fellows securing job placements and estimate the time to placement.

Data:

Anonymized data on Pathrise fellows, including demographics, professional experience, technical skills, mentor interactions, job search activities, and placement outcomes.

Methodology:

Data Preprocessing: Clean and prepare the data for analysis, handling missing values and inconsistencies. Feature Engineering: Create new features to capture more complex relationships between variables (e.g., interaction terms, time-based features). Model Selection and Training: Experiment with various machine learning algorithms (e.g., linear regression, classification models) and tune hyperparameters for optimal performance. Model Evaluation: Assess model accuracy using appropriate metrics (e.g., accuracy, precision, recall, F1-score, mean squared error). Visualization: Use visualizations (e.g., scatter plots, regression plots, confusion matrices, feature importance plots) to understand model behavior and identify key factors influencing placement. Insights and Recommendations:

Identify key factors: Determine the most influential factors affecting job placement success. Optimize program: Provide targeted support based on identified factors to improve fellow outcomes. Estimate time to placement: Offer realistic expectations to fellows regarding the job search process. Continuous improvement: Iterate on the model and data analysis to refine predictions and enhance program effectiveness. Technical Details:

Programming language: Python Libraries: Scikit-learn, pandas, NumPy, matplotlib, seaborn Tools: google colab