You can deploy this model and start earning money today!

SkyLeopard/
placement-season

Name: placement-season
Availability: InStock

sklearn

tabular-classification

random-forest

college_student_placement_dataset

apache-2.0

Views

No views yet

Placement Season Prediction Model

Model Details

Model Name: Placement Season Prediction
Model Type: Random Forest Classifier
Framework: scikit-learn
Language: Python
Task: Binary Classification
License: Apache 2.0
Author: SkyLeopard

This model predicts whether a college student will get placed based on academic and experiential features.

Model Description

This is a supervised machine learning model trained to predict student placement outcome (Yes/No) using structured tabular data. The model uses a Random Forest classifier trained on preprocessed student attributes such as:

IQ
CGPA
Academic performance
Internship experience
Projects
Certifications
Technical and soft skills

The final output is a binary prediction:

1 → Placed
0 → Not Placed

Example Applications

Colleges analyzing placement trends
Students estimating placement probability
Career guidance systems

Limitations

Trained on a single dataset, may not generalize to all institutions.
Performance depends heavily on feature quality.
Should not be used for high-stakes real-world decisions (e.g., hiring, admissions).

Training Data

The model was trained on a structured CSV dataset containing student records with features such as:

Column Name	Type	Description
`College_ID`	Categorical (string)	Unique college/student identifier (dropped during training)
`IQ`	Integer	IQ score of the student
`Prev_Sem_Result`	Float	Previous semester result (GPA/percentage)
`CGPA`	Float	Cumulative Grade Point Average
`Academic_Performance`	Integer	Academic performance rating
`Internship_Experience`	Categorical	Internship experience (`Yes` / `No`)
`Extra_Curricular_Score`	Integer	Score for extracurricular activities
`Communication_Skills`	Integer	Communication skills rating
`Projects_Completed`	Integer	Number of projects completed

Target column:

Placement (mapped to 0/1)

Preprocessing

The following preprocessing steps were applied:

Dropped non-informative ID column.
Categorical mapping:
- Yes → 1
- No → 0
Feature scaling using StandardScaler.
Train-test split.

Evaluation Results

Evaluation performed on a held-out test set.

Metrics used:

Accuracy: ~0.99 (approx)

How to Use

Get model from the hub

python

from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd

REPO_ID = "SkyLeopard/placement-season"
FILENAME = "rf-placement.pkl"

model = joblib.load(
    cached_download(
        hf_hub_url(REPO_ID, FILENAME)
    )
)

# model is a sklearn RandomForestClassifier

Get sample data from this repo

python

data_file = cached_download(
    hf_hub_url(REPO_ID, "college_student_placement_dataset.csv")
)

df = pd.read_csv(data_file)

X = df.drop(["College_ID", "Placement"], axis=1)
y = df["Placement"]

print(X.head(3))

Predict

python

# Encode Internship_Experience manually
X["Internship_Experience"] = X["Internship_Experience"].map({"Yes": 1, "No": 0})

labels = model.predict(X[:3])
print(labels)

Eval

model.score(X, y)

Ethical Considerations

Predictions may reflect biases in the training dataset.
Should not be used to deny opportunities.
Use as a decision-support tool only.

Model Card Authors

SkyLeopard

Citation

If you use this model:

bibtex

@misc{placement_season_model,
  author = {SkyLeopard},
  title = {Placement Season Prediction Model},
  year = {2026},
  publisher = {Hugging Face}
}