Project Customer - 07/01/2023 03:27 EST
Budget: ₹600 – ₹1,500 INR
1. Write a function that takes in a Pandas DataFrame and returns a summary of the distribution of all numeric columns, including mean, median, standard deviation, and quartile information.
2. Write a function that takes in a Pandas DataFrame and returns a summary of the distribution of all categorical columns, including the count, percentage, and 95% confidence interval for each category.
3. Write a function that takes in a Pandas DataFrame and a list of columns, and returns a DataFrame with the columns transformed by a min-max scaling.
4. Using the Matplotlib library, write a function that takes in a Pandas DataFrame and creates a heatmap of the correlation matrix for all numeric columns.
5. Using the Scikit-learn library, write a function that takes in a Pandas DataFrame and fits a random forest model to predict the target column based on all other columns
2. Write a function that takes in a Pandas DataFrame and returns a summary of the distribution of all categorical columns, including the count, percentage, and 95% confidence interval for each category.
3. Write a function that takes in a Pandas DataFrame and a list of columns, and returns a DataFrame with the columns transformed by a min-max scaling.
4. Using the Matplotlib library, write a function that takes in a Pandas DataFrame and creates a heatmap of the correlation matrix for all numeric columns.
5. Using the Scikit-learn library, write a function that takes in a Pandas DataFrame and fits a random forest model to predict the target column based on all other columns