PRODIGY_ML_01

ML Internship at Prodigy InfoTech

Task - 01

Linear Regression model to predict the prices of houses

This repository contains code on Linear Regression model to predict the prices of houses based on their square footage and the number of bedrooms and bathrooms with the dataset from Kaggle Housing Prices Competition.

Description

Addressing real-world challenges, forecasting housing costs is a pivotal issue. Leveraging machine learning, this project endeavors to tackle the problem by delving into the realm of regression techniques. The objective is to analyze the dataset's features and ascertain the most precise model through Linear Regression Model.

Algorithm: Predict House Prices using Linear Regression

Step 1: Import Necessary Libraries

- Import required libraries such as numpy, pandas, matplotlib, seaborn, sklearn.

Step 2: Load the Data

- Read the training dataset ('train.csv') into a pandas DataFrame.
- Read the testing dataset ('test.csv') into a pandas DataFrame.

Step 3: Explore and Preprocess Data

- Explore the data using functions like df.head(), df.shape, df.info(), and df.isnull().sum().
- Handle missing data:
    - Replace missing values in specific columns using mean or mode.
    - Drop unnecessary columns.
- Explore and preprocess categorical features:
    - Handle missing values in categorical columns in both training and test datasets.
    - Perform one-hot encoding for categorical features.

Step 4: Feature Selection

- Select relevant features for the model.
- Analyze correlation between selected features and the target variable ('SalePrice').

Step 5: Data Visualization

- Visualize data using heatmaps and histograms to understand correlations and distributions.

Step 6: Split the Data

- Split the dataset into independent variables (X) and the target variable (y).
- Split the data into training and testing sets.

Step 7: Feature Scaling

- Standardize the feature variables using StandardScaler.

Step 8: Train a Linear Regression Model

- Create a Linear Regression model.
- Train the model using the training set.

Step 9: Make Predictions

- Use the trained model to make predictions on the test set.

Step 10: Evaluate the Model

- Calculate and display the Root Mean Squared Error (RMSE) to evaluate model performance.
- Visualize the predicted values against actual values using scatter plots and residual plots.

Step 11: Additional Analysis

- Analyze and interpret the results, such as the significance of selected features and any patterns in the residuals.

Step 12: Save or Deploy the Model

- If satisfied with the model, save the model for future use or deploy it for predictions.

Usage

1. Dataset from Kaggle Housing Prices Competition.
2. Used Jupiter Notebook for Python Coding.

Abbreviation in the dataset

1. SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.
2. MSSubClass: The building class
3. MSZoning: The general zoning classification
4. LotFrontage: Linear feet of street connected to property
5. LotArea: Lot size in square feet
6. Street: Type of road access
7. Alley: Type of alley access
8. LotShape: General shape of property
9. LandContour: Flatness of the property
10. Utilities: Type of utilities available
11. LotConfig: Lot configuration
12. LandSlope: Slope of property
13. Neighborhood: Physical locations within Ames city limits
14. Condition1: Proximity to main road or railroad
15. Condition2: Proximity to main road or railroad (if a second is present)
16. BldgType: Type of dwelling
17. HouseStyle: Style of dwelling
18. OverallQual: Overall material and finish quality
19. OverallCond: Overall condition rating
20. YearBuilt: Original construction date
21. YearRemodAdd: Remodel date
22. RoofStyle: Type of roof
23. RoofMatl: Roof material
24. Exterior1st: Exterior covering on house
25. Exterior2nd: Exterior covering on house (if more than one material)
26. MasVnrType: Masonry veneer type
27. MasVnrArea: Masonry veneer area in square feet
28. ExterQual: Exterior material quality
29. ExterCond: Present condition of the material on the exterior
30. Foundation: Type of foundation
31. BsmtQual: Height of the basement
32. BsmtCond: General condition of the basement
33. BsmtExposure: Walkout or garden level basement walls
34. BsmtFinType1: Quality of basement finished area
35. BsmtFinSF1: Type 1 finished square feet
36. BsmtFinType2: Quality of second finished area (if present)
37. BsmtFinSF2: Type 2 finished square feet
38. BsmtUnfSF: Unfinished square feet of basement area
39. TotalBsmtSF: Total square feet of basement area
40. Heating: Type of heating
41. HeatingQC: Heating quality and condition
42. CentralAir: Central air conditioning
43. Electrical: Electrical system
44. 1stFlrSF: First Floor square feet
45. 2ndFlrSF: Second floor square feet
46. LowQualFinSF: Low quality finished square feet (all floors)
47. GrLivArea: Above grade (ground) living area square feet
48. BsmtFullBath: Basement full bathrooms
49. BsmtHalfBath: Basement half bathrooms
50. FullBath: Full bathrooms above grade
51. HalfBath: Half baths above grade
52. Bedroom: Number of bedrooms above basement level
53. Kitchen: Number of kitchens
54. KitchenQual: Kitchen quality
55. TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
56. Functional: Home functionality rating
57. Fireplaces: Number of fireplaces
58. FireplaceQu: Fireplace quality
59. GarageType: Garage location
60. GarageYrBlt: Year garage was built
61. GarageFinish: Interior finish of the garage
62. GarageCars: Size of garage in car capacity
63. GarageArea: Size of garage in square feet
64. GarageQual: Garage quality
65. GarageCond: Garage condition
66. PavedDrive: Paved driveway
67. WoodDeckSF: Wood deck area in square feet
68. OpenPorchSF: Open porch area in square feet
69. EnclosedPorch: Enclosed porch area in square feet
70. 3SsnPorch: Three season porch area in square feet
71. ScreenPorch: Screen porch area in square feet
72. PoolArea: Pool area in square feet
73. PoolQC: Pool quality
74. Fence: Fence quality
75. MiscFeature: Miscellaneous feature not covered in other categories
76. MiscVal: $Value of miscellaneous feature
77. MoSold: Month Sold
78. YrSold: Year Sold
79. SaleType: Type of sale
80. SaleCondition: Condition of sale

Techniques

The following techniques are implemented in this project:

• Linear Regression
• Heatmap (correlation graph)