MICROSOFT STOCK ANALAYSIS

Performed exploratory data analysis on Microsoft’s historical stock data to identify trends, detect outliers, and examine correlations among features. Engineered features and trained a Linear Regression model to predict stock closing prices, achieving strong results (R² = 0.99, MSE = 0.83). Visualized stock behavior over time and used heatmaps and scatter plots to derive actionable insights on market movement and feature relationships.

Dataset Taken From: Kaggle

https://www.kaggle.com/datasets/vijayvvenkitesh/microsoft-stock-time-series-analysis

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Tools Used

• Jupyter Notebook

Project Workflow

1. Data Loading.
2. Exploratory Data Analysis (EDA).
3. Data Cleaning (handling nulls, duplicates, outliers).
4. Feature Engineering.
5. Correlation Analysis.
6. Data Type Conversion (e.g., converting Date column).
7. Define Target Variable.
8. Data Splitting (train-test split)
9. Model Building (Linear Regression)
10. Model Training and Testing.
11. Regression Line Visualization.
12. Evaluation Metrics(R² Score, MSE).