https://github.com/isratafrin826/linear-regression-ml-algorithm

https://github.com/isratafrin826/linear-regression-ml-algorithm

googlecolab matplotlib-pyplot numpy pandas

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/isratafrin826/linear-regression-ml-algorithm
• Owner: IsratAfrin826
• Created: 2025-06-19T16:10:30.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-06-19T16:52:33.000Z (12 months ago)
• Last Synced: 2025-06-19T17:29:05.916Z (12 months ago)
• Topics: googlecolab, matplotlib-pyplot, numpy, pandas
• Language: Jupyter Notebook
• Homepage:
• Size: 101 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Linear-Regression-ML-Algorithm

### 📘 Definition:

Linear Regression is a supervised machine learning algorithm used for predicting a continuous dependent variable 

based on one or more independent variables (features). It models the relationship between variables by fitting a 

linear equation to observed data.

### What I have done in this project ---

### 1. Load Dataset

**Objective:** Import the dataset to work with.

**Sources:** CSV, Excel, SQL, or web-based datasets.

**Tools:** pandas.read_csv() or similar functions.

### 2. View / Preprocess Dataset

**Objective:** Understand the structure of data and clean it.

**🔍 Data Exploration**

View top rows: data.head()

Data types: data.dtypes

Shape: data.shape

Summary statistics: data.describe()

**🧹 Data Cleaning**

Handle missing values: fillna(), dropna()

Remove duplicates

Convert categorical to numeric (if needed): One-hot encoding or Label encoding

**📐 Feature Selection**

Choose relevant independent variables (features) and the dependent variable (target).

### 3. Split Dataset

**Objective:** Separate data into training and testing sets to evaluate generalization.

**Tool:** train_test_split() from sklearn.model_selection

### 4. Build the Linear Regression Model

**Objective:** Train the model on the training data.

**Tool:** LinearRegression from sklearn.linear_model

### 5. Test / Evaluation

**Objective:** Make predictions and compare them to actual values.

### 6. Performance Analysis

**Objective:** Quantify how well the model performs.

**📏 Common Metrics:**

Mean Absolute Error (MAE)

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

R-squared Score (R²)

| Step                 | Description                                  |

| -------------------- | -------------------------------------------- |

| Load Dataset         | Import using pandas or other libraries       |

| View/Preprocess      | Clean data, handle nulls, transform features |

| Split Dataset        | Training vs testing data                     |

| Build Model          | Train Linear Regression on training data     |

| Test/Evaluate        | Predict and compare with actual test data    |

| Performance Analysis | Use MAE, MSE, RMSE, R² for evaluation        |