Tools: Colab/Jupyter Notebook, GitHub
Algorithm Category: Regression, Classification
Purpose: Data Cleaning, Apply Algorithm
Algorithm: Logistic Regression, Random Forest Classifier, Support Vector Machines, Decision Tree Classifier, AdaBoost Classifier, Gradient Boosting Classifier
Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, Execdata
Projects: Campus Placement Prediction
Problem Description
This data set consists of Placement data, of students in a XYZ campus. It includes secondary and higher secondary school percentage and specialisation. It also includes degree specialisation, type and Work experience and salary offers to the placed students.We will Analyse what factors are playing a major role in order to select a candidate for job recruitment
Problem Variables
| Field | Description | Unit | dtype | Comments |
|---|---|---|---|---|
| Table 1 | Placement_Data_Full_Class.csv | Table Name | ---------- | |
| sl_no | Serial Number | Continuous | Possible Drop | |
| gender | Gender | Binary | ---------- | |
| ssc_p | Secondary Education Percentage(10th Grade) | Continuous | ---------- | |
| ssc_b | Board of Education | Binary | ---------- | |
| hsc_p | Higher Secondary Education Percentage(12th Grade) | Continuous | ---------- | |
| hsc_b | Board of Eduction | Binary | ---------- | |
| degree_p | Degree Percentage | Continuous | ---------- | |
| degree_t | Undergrad Degree Type(Field of Education) | Non Binary | ---------- | |
| workex | Work Experience | Binary | ---------- | |
| etest_p | Employability Test Percentage(Conducted by College) | Continuous | ---------- | |
| specialisation | Post Grad(MBA) - Specialization | Non Binary | ---------- | |
| mba_p | MBA Percentage | Continuous | ---------- | |
| status | Status of Placement - Placed/Not Placed | Binary | Traget Variable | |
| salary | Salary offered to Corporate Canadidates | Continuous | Possible Drop |
Reference:
Youtube Reference Url: Youtube Video Reference
Dateset:Original Dataset.csv
Demo:Jupyter Notebook/Colab Link
