Data set from UCI Machine Learning Repository. It specifically focuses on forecasting the bike rental demand in the capital bikeshare program combining historical usage pattern with weather data. Then I Carried out series of exploration on the data set specifically a correlation heat map which showed the relationship between the weather data giving an indication of which attributes to drop during Modelling. Using a linear regression model from sklearn we got a mean squared error(MSE) of 0.

First I got my data set from UCI Machine Learning Repository. The data set had 1599 entries with no missing values. Then I Carried out series of exploration on the data set. After several explorations, the physiochemical properties that increases the quality of red wine are; * Alcohol * Sulphates and * Citric Acid Then I used Machine Learning techniques to predict the quality of the wine. After series of testing with 3 different models in classification, random forest classification gave the best result with 0.