# Campus Recruitment: EDA and Classification — Part 1

Day 13 and 14 of 100 Days of Data Science. Welcome back to my 100 Days of Data Science Challenge Journey. On days 13 and 14, I work on Campus Recruitment Dataset available on Kaggle.

Welcome back to my 100 Days of Data Science Challenge Journey. On days 13 and 14, I work on Campus Recruitment Dataset available on Kaggle.

You can read my previous stories here:

This project is going to be divided into two parts. In this part, I will cover the following topics.

### Content:

• Introduction
• Exploring features and relation with the target class

## 1. Introduction

_Campus placement or campus recruiting is a program conducted within universities or other educational institutions to provide jobs to students nearing completion of their studies. — [Wikipedia_](https://en.wikipedia.org/wiki/Campus_placement)

### Problem Statement:

XYZ University wants to build a machine learning model to know whether a student will get placed or not. So that they can provide special attention and help them to get a job. The given dataset can be treated as a classification or regression problem. In this project, I am going to treat this classification problem, where the task is to find whether a candidate will be placed or not. This is a binary classification problem.

``````import numpy as np
import pandas as pd

## data visualization
import matplotlib.pyplot as plt
import seaborn as sns

## setting colors for all graphs
colors = ['#e79c2a','#d54062', '#ebdc87', '#ffa36c']
sns.set_palette(sns.color_palette(colors))``````

``data = pd.read_csv("./Placement_Data_Full_Class.csv")``

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