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.
_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)
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))
Now it’s time to loading dataset in Python DataFrame. Before that, you have to download the dataset to your system. You can download data from here. Now let’s load data.
data = pd.read_csv("./Placement_Data_Full_Class.csv")
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Many a time, I have seen beginners in data science skip exploratory data analysis (EDA) and jump straight into building a hypothesis function or model. In my opinion, this should not be the case.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.