make a e mini$-$project about$($National Rural Livelihoods Mission $)($the data should be from"data.gov in$")$will essentially involve working with a dataset to be imported from either a CSV or Excel file.

should find a suitable dataset and define the problem statement clearly.

The dataset should have a minimum of $1000$ rows and some missing data, outliers, noise, etc.

use the Pandas library and package in Python to work on the project.

The work will be divided into four main stages:

$1.$ Data Cleaning: should be required to clean the dataset by handling missing data appropriately, removing duplicates and outliers, and ensuring consistency in data format. Based on the data sets, you can implement some more cleaning if required.

$2.$ Exploratory Data Analysis $($EDA$)$: After cleaning the data, expected to display the basic statistics about the dataset. you will perform EDA to understand the dataset's distribution, correlation, and relationship between variables. you are expected to visualize their findings in at least five ways, including but not limited to scatter plots, bar charts, histograms, and heat$-$ maps, or any other format they prefer.

$3.$ Feature Selection: Based on their EDA findings, students will select the relevant

features for analysis. Any suitable method of feature selection can be used so that

students can explain why they have selected the features and justify why other features

were excluded.

$4.$ Predictive Modeling: you will use linear or multiple regression to predict the values for the output variable for new inputs. For this, you should divide the dataset into training and test sets, train their model on the training set, and validate the results on the test set. They should also provide the accuracy of their model. you should explain the rationale behind selecting the regression method and interpret the results obtained.

$5.$ Final Report: It should include a detailed explanation of the project's problem statement,

the data cleaning process, the EDA findings, the feature selection process, and the

regression model. you should also include the visualizations they used to communicate

their findings.