## Placement Prediction Using Machine LearningThe application of machine learning has grown in popularity in today's fast-paced society as technology has permeated every aspect of our lives. Placement prediction is one of the numerous uses of machine learning. Using machine learning algorithms, placement prediction determines the likelihood that a student will be hired by a firm based on a variety of criteria, including academic achievement, skill set, and prior job experience. ## Working of Placement Prediction Using Machine Learning- In order to anticipate placement, information is gathered from a variety of sources, including academic transcripts, resumes, and prior job experience.
- After that, this data is cleansed and preprocessed to remove any discrepancies or mistakes.
- After being cleaned, the data is divided into two categories: training data and testing data.
- The machine learning algorithm is trained using the training data, and its effectiveness is assessed using the testing data. The system is taught using a variety of methods, including neural networks, decision trees, and regression analysis.
A statistical method for determining the relationship between two or more variables is A sort of machine learning algorithm known as a decision tree models decisions and potential outcomes using a tree-like structure. The structure and operation of the human brain served as the inspiration for the machine learning algorithm known as neural networks. The algorithm is tested using the testing data once it has been trained to assess its performance. The algorithm's effectiveness is evaluated using a number of measures, including accuracy, precision, recall, and F1 score. These metrics give a sense of how effective the algorithm is in predicting a student's placement likelihood. ## Advantages of Employing Machine Learning for Placement PredictionThere are various advantages of using machine learning algorithms to anticipate placement. - Automating the preliminary screening of candidates cuts down on the time and work needed for the hiring process.
- It offers a recruiting process that is more data-driven and objective, minimizing the influence of subjectivity and prejudice.
- It gives businesses a chance to find people they would have missed via conventional hiring procedures.
## Code ImpelementationHere we try to implement machine learning techniques and methods to find out the relation and patterns of the student who got placed and who did not. ## 1. Importing Libraries## 2. Reading the Dataset
We have 2966 rows with 8 features.
PlacedOrNot is mostly correlated to the CGPA of the student. ## 3. PreprocessingPreprocessing is an important step in machine learning, which means having the data ready and clean before feeding it to the algorithm for learning. Preprocessing is the process of converting raw data into a format appropriate for analysis and modeling. Now, we will be checking if there are any missing or duplicate values in the dataset.
## 4. EDAExploratory data analysis is an important stage in machine learning, which involves examining and visualizing the data to learn more about its composition, traits, and trends. It is carried out prior to developing the actual machine learning model and is crucial for spotting possible difficulties and choosing the right preprocessing and feature engineering strategies.
## 5. RepresentationThe process of statistical representation involves using statistical measures and visualizations to present data in a meaningful and understandable manner with the main objective of enabling the user to understand insights and patterns in the data and make well-informed decisions using the data.
## 6. Encoding Categorical to NumericalIn machine learning, encoding categorical variables to numeric variables is a typical preprocessing step. It requires changing a qualitative attribute-representing category variable into a numerical variable that may be employed in mathematical operations and models.
## 7. Extracting Input and Output Columns
## 9. Scaling the Values## 10. Training & Evaluating the ModelTraining and evaluating the model are the two critical steps in machine learning that determine the accuracy and performance of the model. These steps require careful planning, attention to detail, and rigorous evaluation to develop a model that can generalize well to new, unseen data. Here will go with different machine learning algorithms and find their accuracy.
**Model Selection**
So the
**Tuning the Model**
Using Hyper-Parameter tuning using
**Checking the accuracy of the model with the best parameters**
The accuracy of the model with CV is 83%, and without CV is 80%. We can say that the accuracy of the model that we created is quite high. ## ConclusionThe likelihood that a student will be hired by a firm may be predicted using placement prediction utilizing machine learning techniques. The application of machine learning algorithms offers a more data-driven and objective approach to the hiring process, allowing businesses to find potential applicants who would have gone unnoticed using conventional hiring techniques. Machine learning is becoming more and more prevalent across a wide range of sectors, and placement prediction using machine learning algorithms is poised to become a crucial tool in the hiring process. |