Interest Rate Setting.ipynb

interest-rate-setting.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "Interest Rate Setting.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/firmai/b1eaa531d07a22429e729fe82e4e1e60/interest-rate-setting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdF3tLb2f_h2"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np                   #Importing the required libraries fr the project\n",
        "import matplotlib.pyplot as plt\n",
        "import joblib\n",
        "df=pd.read_csv('https://raw.githubusercontent.com/firmai/random-assets-two/master/mlfin/Credit%20Data.csv') #Importing th dataset from local drive \n",
        "df['Saving accounts'].fillna(df['Saving accounts'].mode().iloc[0],inplace=True)  #Taking care of categorical null values in Saving accounts \n",
        "df['Checking account'].fillna(df['Checking account'].mode().iloc[0],inplace=True)#Taking care of categorical null values in Checking account\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-TWXu1Zf_h9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "611507cf-bb82-4f82-e503-59816821a932"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lb=LabelEncoder()\n",
        "ct=ColumnTransformer([(\"on\",OneHotEncoder(),[1,3,4,5,8])],remainder='passthrough')\n",
        "df=ct.fit_transform(df)\n",
        "df\n",
        "joblib.dump(ct,'onehot.save') # Saving the column transformation"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['onehot.save']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXfSVffrf_h_"
      },
      "source": [
        "from scipy import stats\n",
        "z=np.abs(stats.zscore(df))\n",
        "threshold=3\n",
        "df_no_outliers=df[(z<=threshold).all(axis=1)]\n",
        "x=df[:,:-1]\n",
        "y=df[:,-1]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N27qyUiLf_iB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f59b59b3-d51b-40ad-be9f-2b9840108559"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "x=sc.fit_transform(x)\n",
        "joblib.dump(sc,'Scalar.save') #Saving Scalar transformation"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Scalar.save']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "mi05mT9Ff_iB"
      },
      "source": [
        "Splitting into test and train datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ok76bXekf_iB"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.33,random_state=45)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMtGPfNCf_iD"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsRegressor"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QasC_cHjf_iE"
      },
      "source": [
        "knn_grid=GridSearchCV(estimator=KNeighborsRegressor(),param_grid={'n_neighbors':np.arange(1,20)},cv=5)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGX80lFSf_iE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "249d56b4-41fb-4f42-aa63-4824a932a836"
      },
      "source": [
        "knn_grid.fit(x_train,y_train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
              "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
              "       18, 19])})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIwOaMXUf_iE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4219b90-c664-4e2e-a440-0d3f66e14912"
      },
      "source": [
        "knn_grid.best_params_[\"n_neighbors\"]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ9BLwZyf_iF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c99499e-d292-4cbf-c20b-a94d1f621c52"
      },
      "source": [
        "knn=KNeighborsRegressor(n_neighbors=knn_grid.best_params_[\"n_neighbors\"])\n",
        "knn.fit(x_train,y_train)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=10)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSmj3lxwf_iF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4fd89a5-283b-4aba-bb9b-b337a133555d"
      },
      "source": [
        "y_pred=knn.predict(x_test)\n",
        "y_pred"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0.42799787, 0.30332392, 0.41560373, 0.24831413, 0.39457872,\n",
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              "       0.1592932 , 0.18931551, 0.30942813, 0.36404963, 0.46734986,\n",
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              "       0.30541253, 0.34569675, 0.18373269, 0.33076259, 0.25668453,\n",
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              "       0.19859822, 0.4194761 , 0.38570997, 0.25200123, 0.22157052,\n",
              "       0.31291552, 0.50738655, 0.36312485, 0.39802638, 0.1368117 ,\n",
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              "       0.46739523, 0.48115713, 0.22482146, 0.27898573, 0.24511447,\n",
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              "       0.29220932, 0.22070701, 0.33690258, 0.25989799, 0.22009245])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwzlQkptf_iL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}