A clean, beginner-friendly collection of synthetic student datasets for practicing data analysis, regression, and classification in Python.

This repository contains six CSV datasets designed for learning machine learning concepts with realistic, classroom-style examples. The data focuses on student CGPA, IQ, placement outcomes, and salary/package trends, making it useful for teaching, experimentation, and portfolio projects.

These datasets are ideal for:

• Learning Python and Pandas
• Practicing Exploratory Data Analysis (EDA)
• Building Simple Linear Regression models
• Building Multiple Linear Regression models
• Training Classification models
• Comparing model behavior across small and medium-sized datasets
• Using datasets in Jupyter Notebook, Google Colab, Kaggle, or local Python environments

This dataset contains a single feature (cgpa) and a target variable (package). It is suitable for learning how to model a relationship between a student’s academic performance and their offered package.

• Input feature: cgpa
• Target: package

This dataset adds iq as an additional input feature, allowing learners to explore Multivariate Linear Regression.

• Input features: cgpa, iq
• Target: package

This dataset uses cgpa and iq to predict whether a student is placed or not.

• Input features: cgpa, iq
• Target: placement
• Classes: Yes, No

.
├── README.md
├── cgpa_package_100.csv
├── cgpa_package_300.csv
├── cgpa_iq_package_100.csv
├── cgpa_iq_package_300.csv
├── placement_yes_no_100.csv
├── placement_yes_no_300.csv
├── get-quickly.md
├── Use-any-quickly
└── LICENSE (if present in your branch)

Install the required dependencies:

pip install pandas numpy scikit-learn

Load a dataset directly from GitHub:

import pandas as pd

url = "https://raw.githubusercontent.com/CodeWithAli51/CodeWithAli51/main/cgpa_package_100.csv"
df = pd.read_csv(url)

print(df.head())
print(df.shape)
print(df.columns)

import pandas as pd

url = "https://raw.githubusercontent.com/CodeWithAli51/CodeWithAli51/main/cgpa_iq_package_300.csv"
df = pd.read_csv(url)

print(df.head())
print(df.info())
print(df.describe())
print(df.isnull().sum())

import pandas as pd
from sklearn.linear_model import LinearRegression

url = "https://raw.githubusercontent.com/CodeWithAli51/CodeWithAli51/main/cgpa_package_300.csv"
df = pd.read_csv(url)

X = df[["cgpa"]]
y = df["package"]

model = LinearRegression()
model.fit(X, y)

print("Coefficient:", model.coef_[0])
print("Intercept:", model.intercept_)

import pandas as pd
from sklearn.linear_model import LinearRegression

url = "https://raw.githubusercontent.com/CodeWithAli51/CodeWithAli51/main/cgpa_iq_package_300.csv"
df = pd.read_csv(url)

X = df[["cgpa", "iq"]]
y = df["package"]

model = LinearRegression()
model.fit(X, y)

print(model.coef_)
print(model.intercept_)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

url = "https://raw.githubusercontent.com/CodeWithAli51/CodeWithAli51/main/placement_yes_no_100.csv"
df = pd.read_csv(url)

X = df[["cgpa", "iq"]]
y = df["placement"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

predictions = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, predictions))

This repository helps learners practice:

• Data loading and cleaning
• Feature and target selection
• Exploratory Data Analysis (EDA)
• Linear Regression
• Multiple Regression
• Binary Classification
• Model evaluation and comparison

• Python
• Pandas
• NumPy
• Scikit-learn
• Jupyter Notebook / Google Colab
• CSV-based dataset workflows

All datasets in this repository are synthetic and created for educational and learning purposes only. They do not represent real students, verified salary data, or actual employment records.

Package values are illustrative numerical values and do not imply any real-world financial or job-market meaning. Placement labels are generated for practice and should not be treated as factual information.

These datasets are intended for educational use, experimentation, and personal learning projects. If you reuse or redistribute them, please keep the attribution and synthetic-data disclaimer.

Contributions, suggestions, and improvements are welcome. If you have ideas for additional educational datasets, better documentation, or more example notebooks, feel free to open an issue or submit a pull request.

If you find this repository useful, consider giving it a star on GitHub and sharing it with others learning data science and machine learning.

Happy learning! 🚀