mlessentials/Lab03/Exercise3.04/Exercise3_04.ipynb

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    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "source": [
    "file_url = '../bank-full.csv'\n",
    "bankData = pd.read_csv(file_url, sep=\";\")"
   ]
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    "# Normalising data\n",
    "from sklearn import preprocessing\n",
    "x = bankData[['balance']].values.astype(float)\n",
    "# Creating the scaling function\n",
    "minmaxScaler = preprocessing.MinMaxScaler()\n",
    "# Transforming the balance data by normalising it with minmaxScalre\n",
    "bankData['balanceTran'] = minmaxScaler.fit_transform(x)\n",
    "# Printing the head of the data\n",
    "bankData.head()"
   ]
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    "# Adding a small numerical constant to eliminate 0 values\n",
    "\n",
    "bankData['balanceTran'] = bankData['balanceTran'] + 0.00001"
   ]
  },
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    "# Let us transform values for loan data\n",
    "bankData['loanTran'] = 1\n",
    "# Giving a weight of 5 if there is no loan\n",
    "bankData.loc[bankData['loan'] == 'no', 'loanTran'] = 5\n",
    "bankData.head()"
   ]
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    "# Let us transform values for Housing data\n",
    "bankData['houseTran'] = 5\n",
    "# Giving a weight of 1 if the customer has a house\n",
    "bankData.loc[bankData['housing'] == 'no', 'houseTran'] = 1\n",
    "\n",
    "bankData.head()"
   ]
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   "source": [
    "# Let us now create the new variable which is a product of all these\n",
    "bankData['assetIndex'] = bankData['balanceTran'] * bankData['loanTran'] * bankData['houseTran']\n",
    "bankData.head()"
   ]
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    "# Finding the quantile\n",
    "np.quantile(bankData['assetIndex'],[0.25,0.5,0.75])"
   ]
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    "# Creating quantiles from the assetindex data\n",
    "bankData['assetClass'] = 'Quant1'\n",
    "\n",
    "bankData.loc[(bankData['assetIndex'] > 0.38) & (bankData['assetIndex'] < 0.57), 'assetClass'] = 'Quant2'\n",
    "\n",
    "bankData.loc[(bankData['assetIndex'] > 0.57) & (bankData['assetIndex'] < 1.9), 'assetClass'] = 'Quant3'\n",
    "\n",
    "bankData.loc[bankData['assetIndex'] > 1.9, 'assetClass'] = 'Quant4'\n",
    "\n",
    "bankData.head()"
   ]
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    "# Calculating total of each asset class\n",
    "assetTot = bankData.groupby('assetClass')['y'].agg(assetTot='count').reset_index()\n",
    "# Calculating the category wise counts\n",
    "assetProp = bankData.groupby(['assetClass', 'y'])['y'].agg(assetCat='count').reset_index()"
   ]
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    "# Merging both the data frames\n",
    "assetComb = pd.merge(assetProp, assetTot, on = ['assetClass'])\n",
    "assetComb['catProp'] = (assetComb.assetCat / assetComb.assetTot)*100\n",
    "assetComb"
   ]
  }
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