Titanic Survive Prediction.ipynb

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    "### Author: Abhishek Sharma\n",
    "\n",
    "### Program: Titanic Dataset from Kaggle\n",
    "\n",
    "### Last Modified: August 17, 2017"
   ]
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     "text": [
      "/home/hackpython/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import svm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import KFold\n",
    "import sys\n",
    "train_path = \"data/titanic_survive_prediction_train.csv\"\n",
    "test_path = \"data/titanic_survive_prediction_test.csv\"\n",
    "train_data = pd.read_csv(train_path,delimiter=',')\n",
    "test_data = pd.read_csv(test_path,delimiter=',')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "train_data['Sex'].replace(['male','female'],['0','1'],inplace=True)\n",
    "train_data['Age'].fillna((train_data['Age'].mean()),inplace=True)\n",
    "data = np.array(train_data.iloc[:,4:6])\n",
    "label = np.array(train_data.iloc[:,1:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model: Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "text": [
      "/home/hackpython/anaconda3/lib/python3.5/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
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    {
     "name": "stdout",
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     "text": [
      "0.798882681564\n",
      "0.775280898876\n",
      "0.825842696629\n",
      "0.780898876404\n",
      "0.752808988764\n"
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   "source": [
    "kf = KFold(n=len(data),n_folds=5,shuffle=True)\n",
    "for train_index,test_index in kf:\n",
    "    X_train, X_test = data[train_index], data[test_index]\n",
    "    y_train, y_test = label[train_index], label[test_index]\n",
    "    clf = svm.SVC(kernel='linear',gamma=2)\n",
    "    clf.fit(X_train,y_train)\n",
    "    accuracy=clf.score(X_test,y_test)\n",
    "    print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data['Sex'].replace(['male','female'],[ '0','1'],inplace=True)\n",
    "test_data['Age'].fillna((test_data['Age'].mean()),inplace=True)\n",
    "t_data = np.array(test_data.iloc[:,3:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
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   "source": [
    "clf.predict(t_data)"
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