Campus Placement Data Analysis using Python

By Sai Lokesh

It is a machine learning model based on Python. In this model we are going to Predict the placement status of the students and the expected salary of the students in the placement.

It is a machine learning model based on Python. The dataset is taken from Kaggle(https://www.kaggle.com/searchq=campus+placement+in%3Adatasets)It takes student data as input and classifies whether he will get placed or not. If placed how much salary he can get. Here the algorithm used is RandomForestRegression and LogisticRegression.

It also contains the Exploratory Data Analysis. The numerical features in the data are divided into two parts. i.e Discrete variables and continuous variables and these variables are plotted against the salary to observe how they are affecting the salary.

The outliers are also plotted using BoxPlot.Since there are outliers we have either standardize or normalize the data. Here we standardized the data using StandardScaler.The dependencies are Pandas, NumPy, Matplotlib.

      This model is very helpful for upcoming students to have a clear-cut idea of what is the situation is like out there in the market and it also helps them to know what areas are most important in order to get placed.

As you know we are going to Predict the placement status of the students and the expected salary of the students in the placement.

In the pre-processing of data first, we have used the Numeric Outliers to detects and treats the outliers for each of the selected columns individually by means of the interquartile range (IQR).

First, we have to prepare the data to predict the placement status of the students. So for that first, we have used a column filter to filter some extra column for the data and used the missing value node to fill the missing values in the data.