This repository will feature popular ML algorithms written from scratch

The libraries used for this repository are:

1. Numpy - for scientific and vector computation in python
2. os - for manipulating directory paths
3. matplotlib - to plot data
4. pandas - to read and manipulate.csv files
5. mpl_toolkits.mplot3d - to plot 3d plots
6. csv - to write into csv file

It contains the following algorithms:

1. Linear Regression (completed)
2. Logistic Regression (completed)
3. Backpropagation (completed)
4. K-means Algorithm (to be done)

Analysis_of_appleStore.ipynb : Analysis of data from apple_Store.csv using the above libraries.

LinearRegression.ipynb : uses the data from 'Summary of weather.csv" and predicts the maximum temperature given the minimum temperature or vice-versa using Linear Regression.

LogisticRegressioin.ipynb : uses the data from 'adevertising.csv' and predicts whether a user will click on an add or not using logistic regression with accuracy 95.75%

Backpropagation.ipynb : uses data from 'test.csv' and predicts the label for image using one vs all classification

Apple_Store.csv : contains data of ios app (but unfortunately it doesn't include no. of downloads, so I won't be using this dataset any further)

Advertising.csv : contains data such as no of hours spent on website, daily internet usage, area income, whether the user clicks on add or not

Summary of Weather.csv : contains data like "maximum temperature, minimum temperature etc. "

test.csv : contains data about image pixel (cv problem)

I have included required comments so that a newbie in python having basic programming knowledge can understand