submodules converted to normal repo

Author: Thalaivar

ml

@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count      1460.000000\n",
+       "mean     180921.195890\n",
+       "std       79442.502883\n",
+       "min       34900.000000\n",
+       "25%      129975.000000\n",
+       "50%      163000.000000\n",
+       "75%      214000.000000\n",
+       "max      755000.000000\n",
+       "Name: SalePrice, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('train.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ml/Advanced Regression Techniques/.ipynb_checkpoints/Advanced_Regression_Techniques-checkpoint.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import cross_val_score, train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import RidgeCV, Ridge, LassoCV\n",
+    "from scipy.stats import skew"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = pd.read_csv('train.csv')\n",
+    "# log transform target\n",
+    "train['SalePrice'] = np.log1p(train['SalePrice'])\n",
+    "test = pd.read_csv('test.csv')\n",
+    "data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],test.loc[:,'MSSubClass':'SaleCondition']))\n",
+    "# log transform skewed numeric features\n",
+    "num_features = data.dtypes[data.dtypes != 'object'].index\n",
+    "skewed_feats = train[num_features].apply(lambda x: skew(x.dropna()))\n",
+    "skewed_feats = skewed_feats[skewed_feats > 0.75].index\n",
+    "data = pd.get_dummies(data)\n",
+    "data = data.fillna(data.mean())\n",
+    "data[skewed_feats] = np.log1p(data[skewed_feats])\n",
+    "Xtrain = data[:train.shape[0]].values\n",
+    "Xtest = data[train.shape[0]:].values\n",
+    "y = train.SalePrice.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rmse_cv(model):\n",
+    "    rmse = np.sqrt(-cross_val_score(model, Xtrain, y, scoring='neg_mean_squared_error', cv = 5))\n",
+    "    return(rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_lasso = LassoCV(alphas = [1, 0.1, 0.001, 0.0005], cv = 5).fit(Xtrain, y)\n",
+    "preds = np.expm1(model_lasso.predict(Xtest))\n",
+    "solution = pd.DataFrame({\"id\":test.Id, \"SalePrice\":preds})\n",
+    "solution.to_cs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}