{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
   "name": "analysis.ipynb",
   "provenance": [],
   "collapsed_sections": []
},
"kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
}
},
"cells": [
{
   "cell_type": "markdown",
   "metadata": {
   "id": "X7RUkVitWF5U",
   "colab_type": "text"
   },
   "source": [
   "# [Math/Stats]"
   ]
},
{
   "cell_type": "markdown",
   "metadata": {
   "id": "lOz0m2M8FS9o",
   "colab_type": "text"
   },
   "source": [
   "## 1. Familiarize yourself with the data set. Explain all steps that you did during the exploratory data analysis ‘descriptive statistics’."
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "id": "_jaAlN-4WK0v",
   "colab_type": "code",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 969
   },
   "outputId": "02f8a81a-bc47-4f84-b891-263335ca0d01"
   },
   "source": [
   "import pandas as pd\n",
   "import numpy as np\n",
   "\n",
   "train = pd.read_csv(\"train.csv\")\n",
   "print(train.info())\n",
   "\n",
   "test = pd.read_csv(\"test.csv\")\n",
   "print(test.info())\n",
   "\n",
   "del train['Unnamed: 0']\n",
   "del test['Unnamed: 0']"
   ],
   "execution_count": null,
   "outputs": [
   {
      "output_type": "stream",
      "text": [
         "<class 'pandas.core.frame.DataFrame'>\n",
         "RangeIndex: 2100 entries, 0 to 2099\n",
         "Data columns (total 20 columns):\n",
         " # Column             Non-Null Count Dtype \n",
         "--- ------             -------------- ----- \n",
         " 0 Unnamed: 0          2100 non-null int64 \n",
         " 1 expert_opinion       2100 non-null int64 \n",
         " 2 incorporation_type 2100 non-null object \n",
         " 3 age                2100 non-null int64 \n",
         " 4 continous_feature_1 1761 non-null float64\n",
         " 5 continous_feature_2 1724 non-null float64\n",
         " 6 continous_feature_3 1424 non-null float64\n",
         " 7 continous_feature_4 1761 non-null float64\n",
         " 8 continous_feature_5 1761 non-null float64\n",
         " 9 continous_feature_6 1761 non-null float64\n",
         " 10 continous_feature_7 1757 non-null float64\n",
         " 11 continous_feature_8 1757 non-null float64\n",
         " 12 continous_feature_9 1757 non-null float64\n",
         " 13 continous_feature_10 1761 non-null float64\n",
         " 14 continous_feature_11 1761 non-null float64\n",
         " 15 continous_feature_12 1761 non-null float64\n",
         " 16 continous_feature_13 1545 non-null float64\n",
         " 17 continous_feature_14 1757 non-null float64\n",
         " 18 continous_feature_15 1757 non-null float64\n",
         " 19 categorical_feature_1 2100 non-null object \n",
         "dtypes: float64(15), int64(3), object(2)\n",
         "memory usage: 328.2+ KB\n",
         "None\n",
         "<class 'pandas.core.frame.DataFrame'>\n",
         "RangeIndex: 496 entries, 0 to 495\n",
         "Data columns (total 20 columns):\n",
         " # Column             Non-Null Count Dtype \n",
         "--- ------             -------------- ----- \n",
         " 0 Unnamed: 0          496 non-null int64 \n",
         " 1 expert_opinion       496 non-null int64 \n",
         " 2 incorporation_type 496 non-null object \n",
         " 3 age                496 non-null int64 \n",
         " 4 continous_feature_1 418 non-null float64\n",
         " 5 continous_feature_2 408 non-null float64\n",
         " 6 continous_feature_3 337 non-null float64\n",
         " 7 continous_feature_4 418 non-null float64\n",
         " 8 continous_feature_5 418 non-null float64\n",
         " 9 continous_feature_6 418 non-null float64\n",
         " 10 continous_feature_7 413 non-null float64\n",
         " 11 continous_feature_8 413 non-null float64\n",
         " 12 continous_feature_9 413 non-null float64\n",
         " 13 continous_feature_10 418 non-null float64\n",
         " 14 continous_feature_11 418 non-null float64\n",
         " 15 continous_feature_12 418 non-null float64\n",
         " 16 continous_feature_13 318 non-null float64\n",
         " 17 continous_feature_14 413 non-null float64\n",
         " 18 continous_feature_15 413 non-null float64\n",
         " 19 categorical_feature_1 496 non-null object \n",
         "dtypes: float64(15), int64(3), object(2)\n",
         "memory usage: 77.6+ KB\n",
         "None\n"
      ],
      "name": "stdout"
   }
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "id": "6bYsjqzgXraJ",
   "colab_type": "code",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 272
   },
   "outputId": "a3d0b734-49ff-4281-f203-df5ddab80d86"

Data Science Exercise 1 Introduction Besides a scoring algorithm,...