Project Title: End-to-End Predictive Model Building

???? Objective:

To develop a complete machine learning pipeline that predicts a target outcome (e.g., house price, student performance, customer churn) using structured data.

???? Key Stages of the Project:

Problem Definition:

Define the business or academic question.

Identify the prediction target (e.g., classification or regression).

Data Collection:

Use open datasets (e.g., Kaggle, UCI ML repo) or simulate data.

Example: Titanic dataset, housing prices, exam scores.

Exploratory Data Analysis (EDA):

Use Python (Pandas, Matplotlib, Seaborn) to analyze trends, correlations, and data distributions.

Data Preprocessing:

Handle missing values, outliers, and categorical encoding.

Normalize/standardize numerical features.

Train/test split.

Feature Engineering:

Create meaningful new features or transform existing ones.

Reduce dimensionality if needed (PCA, feature selection).

Model Selection & Training:

Try models like Logistic Regression, Decision Trees, Random Forest, SVM, or XGBoost.

Use cross-validation and hyperparameter tuning (GridSearchCV or RandomizedSearchCV).

Model Evaluation:

Use metrics: Accuracy, Precision, Recall, F1-score (classification) or RMSE, MAE (regression).

Visualize performance with confusion matrix or learning curves.

Model Deployment (Optional):

Save the model with joblib or pickle.

Create a simple Flask or Streamlit web app to serve predictions.

Documentation and Reporting:

Clearly document each step.

Include visuals, code explanations, and conclusions.

???? Tools & Libraries:

Python, Jupyter Notebook

Pandas, NumPy, Scikit-learn

Matplotlib, Seaborn

Streamlit or Flask (for deployment)