Feature Engineering.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction with Univariate Statistical Tests (Chi-squared for classification)\n",
    "from pandas import read_csv\n",
    "from numpy import set_printoptions\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "# load data\n",
    "filename = 'pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "# feature extraction\n",
    "test = SelectKBest(score_func=chi2, k=4)\n",
    "fit = test.fit(X, Y)\n",
    "# summarize scores\n",
    "set_printoptions(precision=3)\n",
    "print(fit.scores_)\n",
    "#features = fit.transform(X)\n",
    "\n",
    "\n",
    "#For regression: f_regression, mutual_info_regression\n",
    "#For classification: chi2, f_classif, mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Extraction with RFE\n",
    "from pandas import read_csv\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# load data\n",
    "filename = 'pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "# feature extraction\n",
    "model = LogisticRegression(max_iter=400)\n",
    "rfe = RFE(model, 3)\n",
    "fit = rfe.fit(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Num Features: \n",
    "fit.n_features_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selected Features:\n",
    "fit.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Ranking:\n",
    "fit.ranking_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance using Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Importance with Extra Trees Classifier\n",
    "from pandas import read_csv\n",
    "from sklearn.tree import  DecisionTreeClassifier\n",
    "# load data\n",
    "filename = 'pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "# feature extraction\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X, Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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