Updated README.md

README.md

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-# BlackFriday-EDA-and-Feature-Engineering
+# Black Friday Dataset: EDA and Feature Engineering
+
+This project focuses on conducting **Exploratory Data Analysis (EDA)** and **Feature Engineering** on the Black Friday dataset. As a beginner, I aimed to apply foundational data science techniques, from cleaning and understanding the dataset to preparing it for model training.
+
+## Project Overview
+
+The Black Friday dataset contains customer purchase details. The main objective of this project is to:
+- Explore the dataset to identify patterns and insights.
+- Engineer features that could help in building predictive models.
+- Prepare the data for training machine learning models.
+
+### Key Steps in the Project
+1. **Data Loading and Preprocessing:**
+   - The dataset was loaded using pandas.
+   - Missing values were handled by separating rows with missing purchase values into test and train sets.
+
+2. **Exploratory Data Analysis (EDA):**
+   - Descriptive statistics were performed to understand the dataset.
+   - Visualizations such as bar plots were used to explore purchase trends and customer demographics.
+
+3. **Feature Engineering:**
+   - Unnecessary columns, like `Product_ID`, were dropped from the feature set.
+   - Numerical features were scaled using **StandardScaler** to prepare for machine learning models.
+
+4. **Train-Test Split:**
+   - The data was split into training and test sets using `train_test_split` from `sklearn`.
+   - The target variable was the `Purchase` column, and all other columns were used as features.
+
+5. **Feature Scaling:**
+   - Implemented scaling of the features to standardize the data, which is essential for certain machine learning models.
+
+### Next Steps
+- The dataset is ready to be used for training machine learning models.
+
+## Libraries Used
+- `pandas`
+- `numpy`
+- `matplotlib`
+- `seaborn`
+- `scikit-learn`
+
+## How to Run the Project
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/YashsTiwari/BlackFriday-EDA-and-Feature-Engineering.git
+   ```
+2. Install the required libraries:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Run the Jupyter notebook to view the analysis.