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    "## LabDS03 ##\n",
    "\n",
    "\n",
    "Suppose you have two datasets with information about movies played in theatres in Portugal (https://github.com/masterfloss/datamovies/raw/main/moviesPT3.xlsx) and ratings of movies obtained from IMDB (https://github.com/masterfloss/datamovies/raw/main/movies_ratings.tsv).\n",
    "\n",
    "Create a model and assess it. Try to have the best accuracy.\n",
    "\n",
    "    1. Business Undertanding \n",
    "    2. Data understanding. Correlation data. Import data and merge two dataframes  \n",
    "    3. Data Preparation. Clean data. \n",
    "    4. Modelling/ model assessment\n",
    "    5. Results Evaluation\n",
    "    6. Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
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   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn   \n",
    "import matplotlib.pyplot as plt   \n",
    "corrMatrix = XY.corr()   \n",
    "sn.heatmap(corrMatrix, annot=True)   \n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "model = sm.OLS(Y, Xfeatures)\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  }
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