Minor Project Ideas for B.tech Computer Science

B.Tech Minor Project: Data Science using Regression

This project uses Linear Regression for continuous prediction and Logistic Regression for binary classification problems such as pass/fail and disease prediction.

   MINOR PROJECT IDEAS 

1. Heart Disease Prediction

Problem:
Classify if a person has heart disease.

Inputs:

• Age

• BP

• Cholesterol

• Heart rate

Output:

• Disease / No disease

  Heart Disease Prediction

  Problem:
  Classify if a person has heart disease.

  Inputs:

  • Age

  • BP

  • Cholesterol

  • Heart rate

  Output:

• Disease / No disease

2. 

2. College Placement Probability Prediction System

(Highly impressive for viva)

Problem

Predict whether a student will get placed + expected salary range.

Models

• Logistic Regression → placed / not placed

• Linear Regression → expected package

Features

• CGPA trend

• Coding test scores

• Internship experience

• Soft-skill ratings

Extra Credit

• Feature importance analysis

• Probability confidence score

3.

3. Financial Credit Risk & EMI Recommendation System

(Industry-oriented)

Problem

Predict:

• Loan approval probability

• Safe EMI amount

Models

• Logistic Regression → loan approval

• Linear Regression → EMI amount

Features

• Income trend

• Expenses

• Credit behavior

• Loan tenure

BIG Add-On

• Risk tiers (Low / Medium / High)

4. 

4. Smart Energy Consumption Forecasting System

(Engineering + Sustainability)

Problem

Forecast electricity consumption & detect over-usage risk.

Models

• Linear Regression → energy units

• Logistic Regression → overload risk

Features

• Appliance usage

• Seasonal effects

• Household size

Outputs

• Monthly forecast

• Warning alerts

5. Smart Traffic Congestion & Accident Risk System

(Engineering + AI)

Problem

Predict:

• Traffic congestion level

• Accident probability

Models

• Linear Regression → congestion index

• Logistic Regression → accident risk

Features

• Vehicle count

• Time of day

• Weather

6. Social Media Misinformation Risk Analyzer

(Trending & Research-oriented)

Problem

Predict whether content is misleading.

Models

• Logistic Regression → fake / real

• Linear Regression → virality score

Features

• Engagement metrics

• Posting time

• Account credibility

BIG VALUE

• Explainable coefficients

• Ethical AI discussion

7. CROP DISEASE PREDICTION SYSTEM

(Data Science Project using Logistic Regression)

🔹 . Problem Statement

Early detection of crop diseases is critical to reduce yield loss and improve agricultural productivity.
This project aims to predict whether a crop is diseased or healthy based on environmental and crop-related parameters using Logistic Regression.

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🔹 . Why this project is “BIG & GOOD”

• Real-world agricultural problem

• Social + economic impact

• Explainable ML (important for farmers)

• Can be extended to yield loss prediction

• Faculty-friendly & industry-relevant

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🔹 . Project Objectives

• Predict disease presence (Yes/No)

• Analyze factors causing disease

• Provide early warning

• (Optional) Predict severity or yield loss

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🔹 . Dataset (Non-image, Data Science based)

Input Features (examples)

• Temperature (°C)

• Humidity (%)

• Rainfall (mm)

• Soil moisture

• Soil pH

• Crop type

• Season

• Fertilizer usage

• Pesticide usage

Output

• Disease (0 = Healthy, 1 = Diseased)

📌 Datasets:

• Kaggle: Crop Disease / Agriculture datasets

• Government agriculture data

• Synthetic dataset (acceptable for minor project)

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🔹 . Machine Learning Models Used

✅ Logistic Regression (Main Model)

Used because:

• Output is binary

• Easy to interpret coefficients

• Works well with tabular data

Equation:

$$P(\text{Disease}) = \frac{1}{1 + e^{-(\beta_0 + \beta_1x_1 + ... + \beta_nx_n)}}$$

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(Optional) Linear Regression

• Predict severity level

• Predict expected yield loss

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🔹 . System Architecture

1. Data Collection

2. Data Preprocessing

3. Feature Selection

4. Logistic Regression Model

5. Prediction

6. Result Visualization

7. Recommendation System

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🔹 . Implementation Flow (Python)

• Load dataset

• Handle missing values

• Train-test split

• Train Logistic Regression model

• Evaluate using:

  • Accuracy

  • Confusion Matrix

  • Precision, Recall

• Plot:

  • Probability curve

  • Feature importance

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🔹 . Results to Show (VERY IMPORTANT)

• Disease prediction accuracy

• Confusion matrix

• Probability vs threshold graph

• Feature impact analysis

• Sample predictions

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🔹 . Future Scope (Makes project BIG)

• Image-based disease detection (CNN)

• IoT sensor integration

• Mobile app for farmers

• Real-time weather API

• Crop recommendation system

10.Intelligent Crop Yield Forecasting System

(Different from disease prediction)

Problem:
Predict crop yield before harvest.

Models:

• Linear Regression → yield (tons/hectare)

• Logistic Regression → low / normal yield risk

Features:
Rainfall, soil nutrients, fertilizer, season

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11.  Air Pollution Level Prediction & Health Risk Alert

Problem:
Predict AQI and classify health risk.

Models:

• Linear Regression → AQI value

• Logistic Regression → hazardous / safe

Features:
PM2.5, PM10, NO₂, SO₂, temperature

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