time-series-classifier.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from tslearn.neighbors import KNeighborsTimeSeriesClassifier, KNeighborsTimeSeries\n",
    "import pickle\n",
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the train and test dataset of time-series values\n",
    "train_data = pd.read_csv(r\"C:\\Users\\i341657\\OneDrive - SAP SE\\Desktop\\fellowship\\fellowship\\final_train_larger_dataframe.csv\") \n",
    "test_data = pd.read_csv(r\"C:\\Users\\i341657\\OneDrive - SAP SE\\Desktop\\fellowship\\fellowship\\Final_test_dataframe.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_data[['x1','x2','x3','x4','x5','x6','x7','x8','x9','x10','x11','x12']]\n",
    "y_train = train_data['Y']\n",
    "x_test = test_data[['x1','x2','x3','x4','x5','x6','x7','x8','x9','x10','x11','x12']]\n",
    "y = test_data['Y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lr = LogisticRegression(penalty='l1',solver='liblinear')\n",
    "Lr.fit(x_train,y_train)\n",
    "y_pred = Lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y.values, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nb = GaussianNB()\n",
    "Nb.fit(x_train,y_train)\n",
    "y_pred = Nb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y.values, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = SVC(kernel=\"linear\",C=0.025,random_state=101)\n",
    "svm.fit(x_train.values, y_train.values)\n",
    "y_pred = svm.predict(x_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y.values, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN with DTW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=3, metric=\"dtw\")\n",
    "knn_clf.fit(x_train.values, y_train.values)\n",
    "\n",
    "filename = 'knn_model.sav'\n",
    "pickle.dump(knn_clf, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading sample test file.\n",
    "api_test = pd.read_csv(r\"C:\\Users\\i341657\\OneDrive - SAP SE\\Desktop\\fellowship\\fellowship\\api_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels = loaded_model.predict(api_test[['x1','x2','x3','x4','x5','x6','x7','x8','x9','x10','x11','x12']].values)\n",
    "accuracy_score(y.values, predicted_labels)"
   ]
  }
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