diff --git a/intern-basics/House_price_Prediction/Readme.md b/intern-basics/House_price_Prediction/Readme.md
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+# House Price Prediction
+
+In this project, we used some algorithm to predict the house price for boston housing datasets. Main goal is to compare various algorithms and evaluate models by comparing prediction accuracy. We examined different models -  Linear Regression, Lasso Regression and RidgeRegression  based on the accuracy (MSE) .
+
+# Algorithms Used
+
+<li>Linear Regression</li>
+<li>Ridge Regression</li>
+<li>Lasso Regression</li>
+
+
+# conclusion
+We have compared algorithms by MSE. We found that Lasso model gave lowest MSE.
diff --git a/intern-basics/House_price_Prediction/boston_house.ipynb b/intern-basics/House_price_Prediction/boston_house.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(506,)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_boston\n",
+    "import numpy as np\n",
+    "\n",
+    "X, Y = load_boston(return_X_y=True)\n",
+    "Y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37.89377859960245\n",
+      "[-1.91246374e-01  4.42289967e-02  5.52207977e-02  1.71631351e+00\n",
+      " -1.49957220e+01  4.88773025e+00  2.60921031e-03 -1.29480799e+00\n",
+      "  4.84787214e-01 -1.54006673e-02 -8.08795026e-01 -1.29230427e-03\n",
+      " -5.17953791e-01] 28.67259959085611\n"
+     ]
+    }
+   ],
+   "source": [
+    "###Linear Regression\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "x_train, x_test = (X[:400], X[400:])\n",
+    "y_train, y_test = (Y[:400], Y[400:])\n",
+    "\n",
+    "reg = linear_model.LinearRegression()\n",
+    "reg.fit(x_train, y_train)\n",
+    "\n",
+    "y_pred = reg.predict(x_test)\n",
+    "print(mean_squared_error(y_pred, y_test))\n",
+    "print(reg.coef_, reg.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "35.364216909603954\n",
+      "[-1.87190620e-01  4.55998256e-02  2.36903825e-02  1.64050920e+00\n",
+      " -7.90352445e+00  4.90320713e+00 -2.77137677e-03 -1.20245094e+00\n",
+      "  4.68016759e-01 -1.63967738e-02 -7.32829766e-01  1.03596933e-03\n",
+      " -5.28859060e-01]\n",
+      "23.378443298143104\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Ridge Regression\n",
+    "ridge = linear_model.Ridge()\n",
+    "ridge.fit(x_train, y_train)\n",
+    "\n",
+    "y_pred_ridge = ridge.predict(x_test)\n",
+    "print(mean_squared_error(y_pred_ridge, y_test))\n",
+    "\n",
+    "print(ridge.coef_)\n",
+    "print(ridge.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21.668677158204268\n",
+      "[-0.06672593  0.04867329 -0.          0.         -0.          1.8287023\n",
+      "  0.02662454 -0.73154672  0.36464622 -0.01739047 -0.6456581   0.00861184\n",
+      " -0.78298032]\n",
+      "34.36296528131457\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Lasso Regression \n",
+    "lasso = linear_model.Lasso()\n",
+    "lasso.fit(x_train, y_train)\n",
+    "\n",
+    "y_pred_lasso = lasso.predict(x_test)\n",
+    "print(mean_squared_error(y_pred_lasso, y_test))\n",
+    "\n",
+    "print(lasso.coef_)\n",
+    "print(lasso.intercept_)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}