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Mathematics/Other material/Housing Price Prediction/Housing-Prediction.ipynb
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Mathematics/Other material/Housing Price Prediction/README.md
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# Housing Price Prediction Model | ||
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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. | ||
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## Usage Guide | ||
- Once the setup is complete, you can use the Housing Price Prediction model with ease: | ||
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- 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. | ||
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- 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. | ||
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- Prediction: Run the prediction script using the command mentioned in the setup. The model will output predicted housing prices based on the input features. | ||
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- 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. | ||
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## 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. | ||
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## 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. | ||
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## 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. | ||
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Mathematics/Other material/Housing Price Prediction/datasets/housing/README.md
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# California Housing | ||
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## 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. | ||
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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). | ||
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## Tweaks | ||
The dataset in this directory is almost identical to the original, with two differences: | ||
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* 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. | ||
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Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing. | ||
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## Data description | ||
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>>> 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 | ||
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>>> housing["ocean_proximity"].value_counts() | ||
<1H OCEAN 9136 | ||
INLAND 6551 | ||
NEAR OCEAN 2658 | ||
NEAR BAY 2290 | ||
ISLAND 5 | ||
Name: ocean_proximity, dtype: int64 | ||
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>>> 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 | ||
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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 | ||
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