Added the Project

Author: MhaskeAditya42

House_Price_Predictor/A icon.png

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09c4931c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from joblib import dump,load\n",
+    "import numpy as np\n",
+    "model=load('Dragon.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c8fecd9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.7])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input=np.array([[0.15682292, -0.4898311 ,  0.98336806, -0.27288841,  0.47919371,\n",
+    "        0.28423269,  0.87020968, -0.68730678,  1.63579367,  1.50571521,\n",
+    "        0.81196637,  0.44624347,  0.81480158]])\n",
+    "model.predict(input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c198ebcb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

House_Price_Predictor/CWP_ Course_project.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "871abc57",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

House_Price_Predictor/Dragon.joblib

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+
+Random Forest Model: 
+Mean 3.279369016724228
+Standred deviation 0.6338795388968552
+
+Linear Regression Model: 
+Mean 5.032945871663797
+Standred deviation 1.0593782704523724
+
+Decesion Tree Model: 
+Mean 4.266164261693081
+Standred deviation 0.7868095840297176
+
+From We can see the Random Forest Model is performing good.

House_Price_Predictor/Model Usage.ipynb

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+1. Title: Boston Housing Data
+
+2. Sources:
+   (a) Origin:  This dataset was taken from the StatLib library which is
+                maintained at Carnegie Mellon University.
+   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the 
+                 demand for clean air', J. Environ. Economics & Management,
+                 vol.5, 81-102, 1978.
+   (c) Date: July 7, 1993
+
+3. Past Usage:
+   -   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 
+       1980.   N.B. Various transformations are used in the table on
+       pages 244-261.
+    -  Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
+       In Proceedings on the Tenth International Conference of Machine 
+       Learning, 236-243, University of Massachusetts, Amherst. Morgan
+       Kaufmann.
+
+4. Relevant Information:
+
+   Concerns housing values in suburbs of Boston.
+
+5. Number of Instances: 506
+
+6. Number of Attributes: 13 continuous attributes (including "class"
+                         attribute "MEDV"), 1 binary-valued attribute.
+
+7. Attribute Information:
+
+    1. CRIM      per capita crime rate by town
+    2. ZN        proportion of residential land zoned for lots over 
+                 25,000 sq.ft.
+    3. INDUS     proportion of non-retail business acres per town
+    4. CHAS      Charles River dummy variable (= 1 if tract bounds 
+                 river; 0 otherwise)
+    5. NOX       nitric oxides concentration (parts per 10 million)
+    6. RM        average number of rooms per dwelling
+    7. AGE       proportion of owner-occupied units built prior to 1940
+    8. DIS       weighted distances to five Boston employment centres
+    9. RAD       index of accessibility to radial highways
+    10. TAX      full-value property-tax rate per $10,000
+    11. PTRATIO  pupil-teacher ratio by town
+    12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks 
+                 by town
+    13. LSTAT    % lower status of the population
+    14. MEDV     Median value of owner-occupied homes in $1000's
+
+8. Missing Attribute Values:  None.
+
+
+