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.

Independently analyze and interpret data to generate actionable insights for business strategy and decision-making.Develop and maintain reports and interactive dashboards using Excel and Tableau to visualize and communicate key performance metrics.Perform data cleaning, transformation, and validation to ensure accuracy and consistency of datasets.Conduct statistical analyses and create data models to identify trends, patterns, and anomalies.Manage end-to-end data analysis projects, including data extraction, aggregation, and presentation.Provide data-driven recommendations and support strategic initiatives through detailed analytical reports.