CASE STUDY:The BostonHousing dataset contains data collected by the US Census Service concerning housing around Boston Massachusetts. It was obtained from the StatLib archive(http://lib.stat.cmu.edu/datasets/boston). The dataset has 167 cases.The data was originally published by Harrison Jr, David, and Daniel L. Rubinfeld. "Hedonic housing prices and the demand for clean air." Journal of environmental economics and management 5.1 (1978):81-102. The BostonHousing.xlsx dataset has 11 attributes. The dataset comes with different imperfections (missing and outliers). As described earlier, most algorithms will not process records with these imperfections

REQUIREMENTS: PART A:- make a review of such techniques, data, and examples with references.

PART B:-Use the provided data file in the following tasks:

1. Except PTRATIO predictor, perform the necessary "Handling Missing Data" operations to the missing values and highlight them with yellow.

2. Find possible "outliers" in the PTRATIO predictor. The possible causes of outliers are:

(a) Typing non-numeric value.

(b) Shift in decimal place while data entry error.

(c) Genuine case of an outlier.

Highlight the cells with outlier cases and state the possible cause indicating a, b, or c.

PART C:-Use the provided data file in the following tasks:

1. Substitute the missing data by NaN (not a number).

2. provide Python code to implement:A. Omission B. Imputation

Deliverables:

A report. Feel free to choose the report format

All the Python code used to develop the models