Final Assignment for Machine Learning with Python.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "In this notebook we try to practice all the classification algorithms that we learned in this course.\n",
    "\n",
    "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
    "\n",
    "Lets first load required libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### About dataset"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "This dataset is about past loans. The __Loan_train.csv__ data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
    "\n",
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
    "| Principal      | Basic principal loan amount at the                                                    |\n",
    "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "| Effective_date | When the loan got originated and took effects                                         |\n",
    "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
    "| Age            | Age of applicant                                                                      |\n",
    "| Education      | Education of applicant                                                                |\n",
    "| Gender         | The gender of applicant                                                               |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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    }
   },
   "source": [
    "Lets download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/local/bin/bash: line 1: wget: command not found\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Load Data From CSV File  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
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    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'loan_train.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m~/.virtualenvs/Coursera/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/Coursera/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/Coursera/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/Coursera/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/Coursera/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'loan_train.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Convert to date time object "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Data visualization and pre-processing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let’s see how many of each class is in our data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "260 people have paid off the loan on time while 86 have gone into collection \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot some columns to underestand data better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# notice: installing seaborn might takes a few minutes\n",
    "# !conda install -c conda-forge seaborn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pre-processing:  Feature selection/extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Lets look at the day of the week people get the loan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
    "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Convert Categorical features to numerical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets look at gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "86 % of female pay there loans while only 73 % of males pay there loan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets convert male to 0 and female to 1:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## One Hot Encoding  \n",
    "#### How about education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Feature befor One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df[['Principal','terms','age','Gender','education']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets defind feature sets, X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our lables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
    "You should use the following algorithm:\n",
    "- K Nearest Neighbor(KNN)\n",
    "- Decision Tree\n",
    "- Support Vector Machine\n",
    "- Logistic Regression\n",
    "\n",
    "\n",
    "\n",
    "__ Notice:__ \n",
    "- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
    "- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
    "- You should include the code of the algorithm in the following cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbor(KNN)\n",
    "Notice: You should find the best k to build the model with the best accuracy.  \n",
    "**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def KNN_cross_val_score_Optimizer(X, y, k):\n",
    "    neigh = KNeighborsClassifier(n_neighbors = k)\n",
    "    return np.asarray(cross_val_score(neigh, X, y, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_MAX = 20\n",
    "KNN_scores = np.zeros((K_MAX,5))\n",
    "for k in range(1, K_MAX+1):\n",
    "    KNN_score = KNN_cross_val_score_Optimizer(X, y, k)\n",
    "    KNN_scores[k-1,:] = KNN_score\n",
    "    \n",
    "score_avg = KNN_scores.mean(axis=1)\n",
    "score_std = KNN_scores.std(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(1,K_MAX+1),score_avg,'g')\n",
    "plt.fill_between(range(1,K_MAX+1), score_avg - 1*score_std, score_avg + 1*score_std, alpha=0.10)\n",
    "plt.fill_between(range(1,K_MAX+1), score_avg - 3*score_std, score_avg + 3*score_std, alpha=0.10, color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.xticks([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])\n",
    "plt.title(\"Best accuracy = \" + str(score_avg.max().round(3)) + \" with k = \" + str(score_avg.argmax()+1))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_model = KNeighborsClassifier(n_neighbors=7)\n",
    "KNN_model.fit(X, y)\n",
    "train_data_score = KNN_model.score(X, y)\n",
    "print('Train data score:', train_data_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def tree_cross_val_score_optimizer(X, y, max_depth):\n",
    "    tree_model = DecisionTreeClassifier(criterion=\"entropy\", max_depth=max_depth)\n",
    "    return np.asarray(cross_val_score(tree_model, X, y, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "K_MAX = 20\n",
    "tree_scores = np.zeros((K_MAX-1,5))\n",
    "for k in range(2, K_MAX+1):\n",
    "    tree_score = tree_cross_val_score_optimizer(X, y, k)\n",
    "    tree_scores[k-2,:] = tree_score\n",
    "    \n",
    "score_avg = tree_scores.mean(axis=1)\n",
    "score_std = tree_scores.std(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(2,K_MAX+1),score_avg,'g')\n",
    "plt.fill_between(range(2,K_MAX+1), score_avg - 1*score_std, score_avg + 1*score_std, alpha=0.10)\n",
    "plt.fill_between(range(2,K_MAX+1), score_avg - 3*score_std, score_avg + 3*score_std, alpha=0.10, color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Depth of the tree')\n",
    "plt.xticks([1, 3, 5, 7, 9, 11, 13, 15, 17, 19])\n",
    "plt.title(\"Best accuracy = \" + str(score_avg.max().round(3)) + \" with depth = \" + str(score_avg.argmax()+2))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_model = DecisionTreeClassifier(criterion=\"entropy\", max_depth=10)\n",
    "tree_model.fit(X, y)\n",
    "train_data_score = tree_model.score(X, y)\n",
    "print('Train data score:', train_data_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def svm_cross_val_score_optimizer(X, y, kernel):\n",
    "    svm_model = svm.SVC(kernel=kernel)\n",
    "    return np.asarray(cross_val_score(svm_model, X, y, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "svm_scores = np.zeros((len(kernels),5))\n",
    "k = 0\n",
    "for kernel in kernels:\n",
    "    print(kernel)\n",
    "    svm_score = svm_cross_val_score_optimizer(X, y, kernel)\n",
    "    svm_scores[k,:] = svm_score\n",
    "    k += 1\n",
    "    \n",
    "score_avg = svm_scores.mean(axis=1)\n",
    "score_std = svm_scores.std(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(0,len(kernels)),score_avg,'g')\n",
    "plt.fill_between(range(0,len(kernels)), score_avg - 1*score_std, score_avg + 1*score_std, alpha=0.10)\n",
    "plt.fill_between(range(0,len(kernels)), score_avg - 3*score_std, score_avg + 3*score_std, alpha=0.10, color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('SVM Kernel')\n",
    "plt.xticks(range(0,len(kernels)), kernels)\n",
    "\n",
    "plt.title(\"Best accuracy = \" + str(score_avg.max().round(3)) + \" with kernel = \" + kernels[score_avg.argmax()])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = svm.SVC(kernel='poly')\n",
    "svm_model.fit(X, y)\n",
    "train_data_score = svm_model.score(X, y)\n",
    "print('Train data score:', train_data_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "def LR_cross_val_score_optimizer(X, y, parameters):\n",
    "    LR_model = LogisticRegression()\n",
    "    LR_grid_search = GridSearchCV(LR_model, parameters)\n",
    "    LR_grid_search.fit(X, y)\n",
    "    return LR_grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'C': [1],\n",
    "          'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}\n",
    "a = LR_cross_val_score_optimizer(X, y, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame.from_dict(a.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_model = LogisticRegression(C=1, solver='sag')\n",
    "LR_model.fit(X, y)\n",
    "train_data_score = LR_model.score(X, y)\n",
    "print('Train data score:', train_data_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download and load the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Test set for evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Principal','terms','age','Gender','weekend']\n",
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.drop(['Unnamed: 0', 'Unnamed: 0.1'], axis=1, inplace=True)\n",
    "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
    "test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
    "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1], inplace=True)\n",
    "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
    "test_df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_df[features]\n",
    "X_test = pd.concat([X_test, pd.get_dummies(test_df['education'])], axis=1)\n",
    "X_test.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocessing.StandardScaler().fit(X_test).transform(X_test)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test_df['loan_status'].values\n",
    "y_test[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report\n",
    "You should be able to report the accuracy of the built model using different evaluation metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN test score\n",
    "y_hat = KNN_model.predict(X_test)\n",
    "\n",
    "KNN_jaccard = jaccard_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('KNN Jaccard-score:', KNN_jaccard)\n",
    "KNN_f1 = f1_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('KNN F1-score:', KNN_f1)\n",
    "\n",
    "print(classification_report(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision tree score\n",
    "y_hat = tree_model.predict(X_test)\n",
    "\n",
    "tree_jaccard = jaccard_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('Decision tree Jaccard-score:', tree_jaccard)\n",
    "tree_f1 = f1_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('Decision tree F1-score:', tree_f1)\n",
    "\n",
    "print(classification_report(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM score\n",
    "y_hat = svm_model.predict(X_test)\n",
    "\n",
    "svm_jaccard = jaccard_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('SVM Jaccard-score:', svm_jaccard)\n",
    "svm_f1 = f1_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('SVM F1-score:', svm_f1)\n",
    "\n",
    "print(classification_report(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic regression score\n",
    "y_hat = LR_model.predict(X_test)\n",
    "y_prob = LR_model.predict_proba(X_test)\n",
    "\n",
    "LR_jaccard = jaccard_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('Logistic regression Jaccard-score:', LR_jaccard)\n",
    "LR_f1 = f1_score(y_test, y_hat, pos_label='PAIDOFF')\n",
    "print('Logistic regression F1-score:', LR_f1)\n",
    "LR_logloss = log_loss(y_test, y_prob)\n",
    "print('Logistic regression LogLoss:', LR_logloss)\n",
    "print(classification_report(y_test, y_hat))"
   ]
  },
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    "| Algorithm          | Jaccard | F1-score | LogLoss |\n",
    "|--------------------|---------|----------|---------|\n",
    "| KNN                | 0.65    | 0.67     | NA      |\n",
    "| Decision Tree      | 0.54    | 0.59     | NA      |\n",
    "| SVM                | 0.74    | 0.74     | NA      |\n",
    "| LogisticRegression | 0.74    | 0.74     | 0.56    |"
   ]
  },
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    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
   ]
  }
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