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Anshika
Anshika

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Building My First End-to-End Machine Learning Project

#programming #ai #deeplearning #beginners

A complete journey from data to deployment with Python, Scikit-learn, and Streamlit

Introduction

As a budding data scientist, I wanted to create a comprehensive machine learning project that showcases the entire ML pipeline - from data preprocessing to model deployment. Today, I'm excited to share my House Price Prediction project that predicts real estate prices using machine learning!

Live Demo: Streamlit app

GitHub Repository: House Price Prediction

Project Overview

This project predicts house prices based on various features like:

  • Median income in the area
  • House age and size characteristics
  • Population and demographic data
  • Geographic location

The goal was to build a real-world applicable model with a user-friendly interface that anyone can use to get instant price predictions.

Tech Stack

  • Python: Core programming language
  • Scikit-learn: Machine learning algorithms
  • Streamlit: Web application framework
  • Pandas & NumPy: Data manipulation
  • Matplotlib & Seaborn: Data visualization
  • Plotly: Interactive charts

The Dataset

I used the California Housing Dataset containing 20,640 samples with features like:

  • Median income
  • House age
  • Average rooms/bedrooms
  • Population density
  • Geographic coordinates

This dataset is perfect for learning because it's:

  • Real-world data
  • Clean and well-structured
  • Sufficient size for training
  • Interpretable features

Key Steps in My ML Pipeline

1. Exploratory Data Analysis (EDA)

First, I dove deep into understanding the data:

# Check data distribution
plt.figure(figsize=(15, 10))
df.hist(bins=30, alpha=0.7)
plt.suptitle('Feature Distributions')
plt.show()

# Correlation analysis
correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
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Key Insights:

  • Median income has the strongest correlation with price (0.69)
  • Location (latitude/longitude) significantly impacts pricing
  • House age has a moderate negative correlation

2. Feature Engineering

I created three new features to improve model performance:

# Engineer new features
df['rooms_per_household'] = df['AveRooms'] / df['AveOccup']
df['population_per_household'] = df['Population'] / df['HouseHolds']
df['bedrooms_per_room'] = df['AveBedrms'] / df['AveRooms']
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These engineered features provided better insights into housing quality and density.

3. Data Preprocessing 

# Handle outliers using IQR method
Q1 = df['price'].quantile(0.25)
Q3 = df['price'].quantile(0.75)
IQR = Q3 - Q1
df_clean = df[(df['price'] >= Q1 - 1.5*IQR) & (df['price'] <= Q3 + 1.5*IQR)]

# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
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4. Model Training & Evaluation

I chose Linear Regression for interpretability:

model = LinearRegression()
model.fit(X_train_scaled, y_train)

# Evaluate performance
test_r2 = r2_score(y_test, y_pred)
test_rmse = np.sqrt(mean_squared_error(y_test, y_pred))

print(f"R² Score: {test_r2:.4f}")
print(f"RMSE: ${test_rmse*100:.0f}k")
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Model Performance:

  • R² Score: 0.60 (explains 60% of price variance)
  • RMSE: ~$68k
  • MAE: ~$50k

Building the Web Application

The most exciting part was creating an interactive web app using Streamlit.

App Features:

  • Interactive sliders for all input features
  • Real-time predictions with instant results
  • Visualization of results and comparisons
  • Feature importance explanations
  • Mobile-responsive design

Results & Insights

Model Performance

  • Successfully predicts house prices with 60% accuracy
  • Identifies median income as the strongest price predictor
  • Location factors (lat/long) significantly impact pricing
  • Engineered features improved model performance by 5%

Key Learnings

  1. Feature engineering can significantly boost model performance
  2. Data visualization is crucial for understanding patterns
  3. Model interpretability is as important as accuracy
  4. User experience matters in ML applications

Future Improvements

  1. Advanced Algorithms: Implement Random Forest, XGBoost
  2. Hyperparameter Tuning: Use GridSearchCV for optimization
  3. Cross-Validation: Implement k-fold cross-validation
  4. Real-time Data: Integrate with real estate APIs
  5. Model Monitoring: Add performance tracking
  6. Cloud Deployment: Deploy on AWS/GCP for scalability

Lessons Learned

Technical Lessons

  • Data quality is more important than model complexity
  • Feature engineering often beats algorithm selection
  • Model interpretability is crucial for business applications
  • User interface design significantly impacts adoption

Project Management

  • Documentation is essential for portfolio projects
  • Version control (Git) saves time and prevents disasters
  • Modular code makes debugging and improvements easier
  • Testing with sample data prevents deployment issues

Impact on My Learning Journey

This project has significantly enhanced my skills in:

  • End-to-end ML pipeline development
  • Data preprocessing and feature engineering
  • Model evaluation and interpretation
  • Web application development
  • Project documentation and presentation
  • Version control and collaboration

I'd love to hear your thoughts! Please:

  • Star the GitHub repository if you find it useful
  • Comment with suggestions or questions
  • Share if you think others might benefit

Connect with me:

What's your first ML project story? Share in the comments below! 👇

This post chronicles my journey building my first complete ML project. The code, data, and live demo are all available for you to explore, learn from, and build upon. Happy coding!

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