Write Python code for any given csv dataset with some $20-25$ columns and around $2000$ rows, Include explaination and suggest alternatives $-$ make it simple to understand and write.

#### $1.$ Data Understanding $(5$ marks$)$

a$.$ Read the dataset $($tab$,$ csv$,$ xls$,$ txt$,$ inbuilt dataset$).$ What are the number of rows and no$.$ of cols & types of variables $($continuous$,$ categorical etc.$)?(1$ MARK$)$

b$.$ Calculate five$-$point summary for numerical variables $(1$ MARK$)$

c$.$ Summarize observations for categorical variables $$ no$.$ of categories, $\%$ observations in each category. $(1$ marks$)$

d$.$ Check for defects in the data such as missing values, null, outliers, etc. $(2$ marks$)$

#### $2.$ Data Preparation $(15$ marks$)$

a$.$ Fix the defects found above and do appropriate treatment if any. $(5$ marks$)$

b$.$ Visualize the data using relevant plots. Find out the variables which are highly correlated with target variable? $(5$ marks$)$

c$.$ Do you want to exclude some variables from the model based on this analysis? What other actions will you take? $(2$ marks$)$

d$.$ Split dataset into train and test $(70$:$30).$ Are both train and test representative of the overall data? How would you ascertain this statistically? $(3$ marks$)$

### $3.$ Model Building $(20$ marks$)$

a$.$ Fit a base model and observe the overall R$-$ Squared, RMSE and MAPE values of the model. Please comment on whether it is good or not. $(5$ marks$)$

b$.$ Check for multi$-$collinearity and treat the same. $(3$ marks$)$

c$.$ How would you improve the model? Write clearly the changes that you will make before re$-$fitting the model. Fit the final model. $(6$ marks$)$

d$.$ Write down a business interpretation$/$explanation of the model $$ which variables are affecting the target the most and explain the relationship. Feel free to use charts or graphs to explain. $(4$ marks$)$

e$.$ What changes from the base model had the most effect on model performance? $(2$ marks$)$