![Thank You for Your
Attention!
Appreciation:
We appreciate your time and interest in this project.
If you have any questions or need further information, feel free to reach
out.
Contact Information:
Email: [dacchu143143@email.com]
Questions and Discussion:
Open the floor for any questions, feedback, or discussion regarding the
project.](https://image.slidesharecdn.com/autompgprediction-241130085206-bbc462ff/85/Auto-MPG-prediction-pptx-solar-energy-generation-prediction-11-320.jpg)
Auto MPG prediction.pptx solar energy generation prediction
- 1. G.MADEGOWDA INSTITUTE OF TECHNOLOGY Bharathinagara, Maddur taluk, Mandya Dist-571422 DEPARTMENT OF ELECTRICALAND ELECTRONICS ENGINEERING THE PROJECT REPORT ON “ AUTO MPG PRIDICTION ” EEE Under the guidance of : Mr . Hemanth kumar A Senior DataScientist , Brillio
- 2. 02 SRINIVAS EDIGA K.P 03 DARSHAN.T 04 PUNITH.GR DARSHAN.RS 01 Submitted In Partial Fulfillment Of The Requirement By : 05 DARSHAN.M 05 Contents : Introduction Advantages of the Project System Requirements Software Requirements Conclusion
- 3. Introduction to AUTO MPG GAS PREDICTION Project Objective The primary goal is to develop a machine learning model that predicts a vehicle's MPG (a measure of fuel efficiency) using input features. Accurate predictions can help manufacturers design more fuel-efficient vehicles and assist consumers in making informed decisions.
- 4. Steps in the Project Problem Definition: ◦Understand the relationship between various vehicle attributes and MPG. Identify the significance of predicting MPG for automotive industries and consumers.
- 5. 2. Data Collection • Dataset: Use the Auto MPG Dataset, which is publicly available, for example, through the UCI Machine Learning Repository. • Attributes in the Dataset: • MPG (target variable) • Cylinders (number of engine cylinders) • Displacement (engine size in cubic inches) • Horsepower (engine power) • Weight (vehicle weight in pounds) • Acceleration (time to accelerate from 0 to 60 mph in seconds) • Model year
- 6. 3. Data Preprocessing • Handling Missing Values: Fill or remove records with missing horsepower values or others. • Data Transformation: Normalize or scale features to ensure uniformity. • Feature Engineering: • Encode categorical variables like "Origin" (e.g., using one-hot encoding). • Create interaction terms or polynomial features if needed. • Exploratory Data Analysis (EDA): • Visualize relationships between MPG and other features using scatterplots, histograms, and heatmaps.
- 7. . . •4.Model Selection: •Linear Regression •Decision Trees or Random Forests •Gradient Boosting Models (e.g., XGBoost, LightGBM) •Neural Networks (for more complex patterns) •Hyperparameter Tuning: Optimize model performance using techniques like grid search or random search. 5. Model Evaluation •Use metrics such as: •Mean Absolute Error (MAE) •Mean Squared Error (MSE) •R-squared (R²) •Evaluate on unseen data to check for overfitting or underfitting. 5
- 8. 6. Deployment •Deploy the trained model as a web app or API using tools like Flask, FastAPI, or Streamlit. •Allow users to input vehicle specifications and get MPG predictions 7. Insights and Recommendations •Interpret model results to identify key factors affecting MPG. •Provide actionable insights to manufacturers (e.g., lighter vehicles tend to have higher MPG). •Highlight trends (e.g., newer model years may have better fuel efficiency). .
- 9. 8. Future Enhancements •Incorporate real-time data from IoT or connected vehicles. •Extend the model to predict other performance metrics like emissions or maintenance costs. Let me know if you want detailed guidance on any of these steps!
- 10. Software Requirements • Operating System: Windows 10/11, macOS, or Linux • Programming Languages: Python (for machine learning models) • Libraries/Frameworks: > Machine Learning: Scikit-learn, TensorFlow, Keras > Data Processing: Pandas, NumPy > Visualization: Matplotlib, Seaborn • Database: MySQL, Kaggel • IDE: Jupyter Notebook, PyCharm, or VS Code • Others: Python 3.x, pip for managing libraries
- 11. Thank You for Your Attention! Appreciation: We appreciate your time and interest in this project. If you have any questions or need further information, feel free to reach out. Contact Information: Email: [dacchu143143@email.com] Questions and Discussion: Open the floor for any questions, feedback, or discussion regarding the project.