Machine Learning Intern
Data Exploration & Preprocessing: Conducted thorough data analysis on the Amazon stock price dataset, ensuring data integrity by handling 100% of missing values using mean imputation. Standardized features via StandardScaler for consistent model input.Exploratory Data Analysis (EDA): Performed in-depth EDA, visualizing key relationships between features and the target variable (Amazon_Price). Developed scatter plots, histograms, and correlation matrices to gain insights.Feature Engineering: Enhanced the dataset by transforming raw features, improving model performance and interpretability. Analyzed the impact of features on target price prediction.Model Development: Implemented a Random Forest Regression model, achieving an 80-20 split for training and testing data. Evaluated performance using:Mean Absolute Error (MAE): 0.0697Root Mean Squared Error (RMSE): 0.0961Mean Squared Error (MSE): 0.01, identifying areas for improvement.Cross-Validation & Testing: Applied 5-fold cross-validation, achieving a Mean MSE of 0.81 with a standard deviation of 0.88, indicating consistent performance. Tested the model on unseen data, fine-tuning based on evaluation metrics such as MAE and RMSE.Feature Importance Analysis: Identified key predictors influencing Amazon price fluctuations through regression coefficients, visualized using bar plots for stakeholder insights.
