PDSH_Bike sharing Demand_2.ipynb

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       "'\\nclassification\\nclustering\\nregression\\ndimensionality reduction\\n\\n\\nRMSLE\\n\\n'"
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    "## scikit learn \n",
    "\"\"\"\n",
    "classification\n",
    "clustering\n",
    "regression\n",
    "dimensionality reduction\n",
    "\n",
    "\"\"\""
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       "make_scorer(rmsle)"
      ]
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   "source": [
    "## RMSLE \n",
    "\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "\n",
    "def rmsle(predicted_values, actual_values):\n",
    "    # to make array\n",
    "    predicted_values=np.array(predicted_values)\n",
    "    actual_values=np.array(actual_values)\n",
    "    \n",
    "    # add 1 to predicted values , actual values and to take log \n",
    "    log_predict=np.log(predicted_values+1)\n",
    "    log_actual=np.log(actual_values+1)\n",
    "    \n",
    "    # square the difference\n",
    "    difference = log_predicted-log_actual\n",
    "    difference = np.square(difference)\n",
    "    \n",
    "    \n",
    "    # mean \n",
    "    mean_difference=difference.mean()\n",
    "    \n",
    "    # to take log\n",
    "    score=np.sqrt(mean_difference)\n",
    "    \n",
    "    return score\n",
    "\n",
    "rmsle_scorer=make_scorer(rmsle)\n",
    "rmsle_scorer\n",
    "    "
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   "source": [
    "#cross validation\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "k_fold=KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
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