1. GRADIENT DESCENT PROJECT ON BOSTON DATASET:

   DATASET: Boston dataset is one of the datasets available in sklearn. You are given a Training dataset csv file with X train and Y train data. As studied in lecture, your task is to come up with Gradient Descent algorithm and thus predictions for the test dataset given.

   Your task is to:

   1. Code Gradient Descent for N features and come with predictions.
   2. Try and test with various combinations of learning rates and number of iterations.
   3. Try using Feature Scaling, and see if it helps you in getting better results.

   Read Instructions carefully -

   1. Use Gradient Descent as a training algorithm and submit results predicted.
   2. Files are in csv format, you can use genfromtxt function in numpy to load data from csv file. Similarly you can use savetxt function to save data into a file.
   3. Submit a csv file with only predictions for X test data. File name should not have spaces. File should not have any headers and should only have one column i.e. predictions. Also predictions shouldn't be in exponential form.
   4. Your score is based on coefficient of determination.

2.GRADIENT DESCENT PROJECT ON Combined Cycle Power Plant:

Combined Cycle Power Plant dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set 	to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum 	       (V) to predict the net hourly electrical energy output (EP) of the plant.

You are given:

1. A Readme file for more details on dataset.
2. A Training dataset csv file with X train and Y train data
3. A X test File and you have to predict and submit predictions for this file.

Your task is to:

1. Code Gradient Descent for N features and come with predictions.
2. Try and test with various combinations of learning rates and number of iterations.
3. Try using Feature Scaling, and see if it helps you in getting better results.

Read Instructions carefully -

1. Use Gradient Descent as a training algorithm and submit results predicted.
2. Files are in csv format, you can use genfromtxt function in numpy to load data from csv file. Similarly you can use savetxt function to save data into a 	    file.
3. Submit a csv file with only predictions for X test data. File should not have any headers and should only have one column i.e. predictions. Also predictions 	shouldn't be in exponential form.
4. Your score is based on coefficient of determination. So it can be possible that nobody gets full score.

3. A SIMPLE GRADIENT DESCENT CODE FILE CONTAINING BASIC FUNCTIONS FOR GRADIENT DESCENT.

THIS TASK WAS GIVEN BY CODING NINJAS , THEREFORE ALL THE PREDICTIONS WERE SUBMITTED TO CODING NINJAS.

SCORE GIVEN BY CODING NINJAS TO MY PREDICTIONS:

GRADIENT DESCENT ON BOSTON DATASET : 301.7/400 GRADIENT DESCENT ON COMBINED CYCLE DATASET : 371.4/400