Merge pull request #75 from Maykkkk/Maykkkk-patch-1

Added Other Materials

Mathematics/Other material/Housing Price Prediction/README.md

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+# Housing Price Prediction Model
+
+Welcome to the Housing Price Prediction model. This machine learning model is designed to predict housing prices using a dataset of various features related to houses. Whether you're a beginner or an experienced practitioner, this project serves as a great starting point to delve into machine learning for real estate.
+
+## Usage Guide
+- Once the setup is complete, you can use the Housing Price Prediction model with ease:
+
+- Data Preparation: Make sure you have a dataset prepared in the same format as the example data provided. Ensure that the features match the columns used during training.
+
+- Model Loading: If you want to use a pre-trained model, update the model_path variable in predict_prices.py to point to the location of your saved model.
+
+- Prediction: Run the prediction script using the command mentioned in the setup. The model will output predicted housing prices based on the input features.
+
+- Interpret Results: Analyze the predicted prices and assess the model's performance. You can further fine-tune the model parameters or features to improve its accuracy.
+
+## Model Details
+The Housing Price Prediction model is built upon the Scikit-Learn library, utilizing powerful regression techniques. It's designed to predict housing prices based on features such as square footage, number of bedrooms, location, and more. The model has been trained on a real-world dataset, allowing it to provide valuable insights into housing market trends.
+
+## Dataset
+The dataset used for training and testing this model is not included in this repository due to its size. You can find the dataset and its description in Chapter X of "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. Please download the dataset from the provided source and ensure it's appropriately formatted before use.
+
+## Contributing
+Contributions to this project are more than welcome! If you find any issues, have suggestions, or want to add new features, please feel free to open an issue or submit a pull request.
+
+Reg no.- 22BCE10275

Mathematics/Other material/Housing Price Prediction/datasets/housing/README.md

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+# California Housing
+
+## Source
+This dataset is a modified version of the California Housing dataset available from [Luís Torgo's page](http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html) (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.
+
+This dataset appeared in a 1997 paper titled *Sparse Spatial Autoregressions* by Pace, R. Kelley and Ronald Barry, published in the *Statistics and Probability Letters* journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).
+
+## Tweaks
+The dataset in this directory is almost identical to the original, with two differences:
+
+* 207 values were randomly removed from the `total_bedrooms` column, so we can discuss what to do with missing data.
+* An additional categorical attribute called `ocean_proximity` was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data.
+
+Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing.
+
+## Data description
+
+    >>> housing.info()
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 20640 entries, 0 to 20639
+    Data columns (total 10 columns):
+    longitude             20640 non-null float64
+    latitude              20640 non-null float64
+    housing_median_age    20640 non-null float64
+    total_rooms           20640 non-null float64
+    total_bedrooms        20433 non-null float64
+    population            20640 non-null float64
+    households            20640 non-null float64
+    median_income         20640 non-null float64
+    median_house_value    20640 non-null float64
+    ocean_proximity       20640 non-null object
+    dtypes: float64(9), object(1)
+    memory usage: 1.6+ MB
+
+    >>> housing["ocean_proximity"].value_counts()
+    <1H OCEAN     9136
+    INLAND        6551
+    NEAR OCEAN    2658
+    NEAR BAY      2290
+    ISLAND           5
+    Name: ocean_proximity, dtype: int64
+
+    >>> housing.describe()
+              longitude      latitude  housing_median_age   total_rooms  \
+    count  16513.000000  16513.000000        16513.000000  16513.000000   
+    mean    -119.575972     35.639693           28.652335   2622.347605   
+    std        2.002048      2.138279           12.576306   2138.559393   
+    min     -124.350000     32.540000            1.000000      6.000000   
+    25%     -121.800000     33.940000           18.000000   1442.000000   
+    50%     -118.510000     34.260000           29.000000   2119.000000   
+    75%     -118.010000     37.720000           37.000000   3141.000000   
+    max     -114.310000     41.950000           52.000000  39320.000000   
+
+           total_bedrooms    population    households  median_income  
+    count    16355.000000  16513.000000  16513.000000   16513.000000  
+    mean       534.885112   1419.525465    496.975050       3.875651  
+    std        412.716467   1115.715084    375.737945       1.905088  
+    min          2.000000      3.000000      2.000000       0.499900  
+    25%        295.000000    784.000000    278.000000       2.566800  
+    50%        433.000000   1164.000000    408.000000       3.541400  
+    75%        644.000000   1718.000000    602.000000       4.745000  
+    max       6210.000000  35682.000000   5358.000000      15.000100
+