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    "## LabDS02 & LabDS03   ##\n",
    "\n",
    "Consider the following csv, that has information about movies:\n",
    "\n",
    "https://github.com/masterfloss/datamovies/raw/main/movies_ratings.tsv\n",
    "\n",
    "https://github.com/masterfloss/datamovies/raw/main/moviesPT3.xlsx\n",
    "\n",
    "Continue the work performed for LabDS01.\n",
    "Perform the follwoing group of activity:\n",
    "\n",
    "- Modelling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For regression, use the follwoing libraies\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, median_absolute_error, explained_variance_score,r2_score\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For classification, use the follwoing libtaries\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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