python programmer for data science

From a dataset of natural gas consumption data from residential consumers, using a Jupyter notebook, explore the data and forecast using linear regression model and support vector regression. Requirement needed from the dataset: 1)Identify number of houses are included 2)Any malfunctioning meters, if so identify them and the time periods of malfunction. 3) Generate hour reading from raw data, selecting 1 month from the 6 month study interval and plot hourly reading. 4)Intuitively, we expect that gas consumption from different homes to be correlated. For example, many homes would experience higher consumption levels in the evening when meals are cooked. For each home, find the top five homes with which it shows the highest correlation 5)Build a model to forecast the hourly readings in the future (next hour). Can you explain why you may want to forecast the gas consumption in the future? Who would find this information valuable? What can you do if you have a good forecasting model? 6)Build a linear regression model to forecast the hourly readings in the future (next hour). Generate two plots: (i) Time series plot of the actual and predicted hourly meter readings and (ii) Scatter plot of actual vs predicted meter readings (along with the line showing how good the fit is). 7) Same as 6 but using support vector regression. 8) Propose and carry out additional analysis using the dataset given. Please be sure to justify why this additional analysis is useful and interesting.