Udacity - Machine learning Nano Degree Program : Project-1
This is second project in the series of the projects listed in Udacity- Machine Learning Nano Degree Program.
In this project, i have applied basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home. I have first explored the data to obtain important features and descriptive statistics about the dataset. Next, I have properly split the data into testing and training subsets, and determined a suitable performance metric for this problem. I have then analyzed performance graphs for a learning algorithm with varying parameters and training set sizes. Thus enabling me to pick the optimal model that best generalizes for unseen data. Finally, I have tested this optimal model on a new sample and compared the predicted selling price to my statistics.
This project is designed to get me acquainted to working with datasets in Python and applying basic machine learning techniques using NumPy and Scikit-Learn.
● Achieved accuracy of 90.02 % .
Things i have learnt by completing this project:
- How to use NumPy to investigate the latent features of a dataset.
- How to analyze various learning performance plots for variance and bias.
- How to determine the best-guess model for predictions from unseen data.
- How to evaluate a model’s performance on unseen data using previous data.
- Project 0 : Titanic Survivals Prediction
- Project 2 : Charity Donors Prediction
- Project 3 : Creating Cutomer Segments
- Project 4 : Smart Cab
- Project 5 : ImageNetBot
- Project 6 : Stock Price Predictor
This project uses the following software and Python libraries: