Applications Submitted Prediction_Regression.ipynb

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   "source": [
    "#importing libraries\n",
    "import csv\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "import tkinter as tk\n",
    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
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     "text": [
      "        Date  YearCreated  MonthCreated  DayCreated  CaseCount  \\\n0  10/1/2019         2019            10           1          1   \n1  10/3/2019         2019            10           3          2   \n2  10/4/2019         2019            10           4          6   \n3  10/7/2019         2019            10           7          1   \n4  10/8/2019         2019            10           8         18   \n\n   App Form Downloaded Case Count  Case Submitted Count  \n0                               1                     0  \n1                               2                     2  \n2                               2                     2  \n3                               1                     1  \n4                              13                     7  \nDate\nYearCreated\nMonthCreated\nDayCreated\nCaseCount\nApp Form Downloaded Case Count\nCase Submitted Count\n"
     ]
    }
   ],
   "source": [
    "f=open(\"OPSDataset.csv\", \"r\")\n",
    "df = pd.read_csv(\"OPSDataset.csv\") \n",
    "print (df.head())\n",
    "for col in df.columns: \n",
    "    print(col)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 81,
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "new_df = pd.DataFrame(df,columns=['Date','DayCreated','CaseCount','App Form Downloaded Case Count','Case Submitted Count'])\n",
    " \n",
    "plt.scatter(df['CaseCount'], df['Case Submitted Count'], color='red')\n",
    "plt.title('Case Submitted Count V/S CaseCount', fontsize=14)\n",
    "plt.xlabel('CaseCount', fontsize=14)\n",
    "plt.ylabel('Case Submitted Count', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "new_df = pd.DataFrame(df,columns=['Date','DayCreated','CaseCount','App Form Downloaded Case Count','Case Submitted Count'])\n",
    " \n",
    "plt.scatter(df['App Form Downloaded Case Count'], df['Case Submitted Count'], color='red')\n",
    "plt.title('Case Submitted Count V/S App Form Downloaded Case Count', fontsize=14)\n",
    "plt.xlabel('App Form Downloaded Case Count', fontsize=14)\n",
    "plt.ylabel('Case Submitted Count', fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Intercept: \n 0.22125322693510485\nCoefficients: \n [-0.08120927  0.66541104]\nPredicted Case Submitted Count: \n [7.80013933]\n                             OLS Regression Results                             \n================================================================================\nDep. Variable:     Case Submitted Count   R-squared:                       0.869\nModel:                              OLS   Adj. R-squared:                  0.863\nMethod:                   Least Squares   F-statistic:                     152.5\nDate:                  Sun, 22 Nov 2020   Prob (F-statistic):           5.04e-21\nTime:                          15:54:20   Log-Likelihood:                -70.312\nNo. Observations:                    49   AIC:                             146.6\nDf Residuals:                        46   BIC:                             152.3\nDf Model:                             2                                         \nCovariance Type:              nonrobust                                         \n==================================================================================================\n                                     coef    std err          t      P>|t|      [0.025      0.975]\n--------------------------------------------------------------------------------------------------\nconst                              0.2213      0.228      0.969      0.338      -0.238       0.681\nCaseCount                         -0.0812      0.100     -0.812      0.421      -0.283       0.120\nApp Form Downloaded Case Count     0.6654      0.114      5.833      0.000       0.436       0.895\n==============================================================================\nOmnibus:                       11.192   Durbin-Watson:                   1.912\nProb(Omnibus):                  0.004   Jarque-Bera (JB):               17.197\nSkew:                           0.646   Prob(JB):                     0.000184\nKurtosis:                       5.599   Cond. No.                         15.7\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.DataFrame(df,columns=['Date','DayCreated','CaseCount','App Form Downloaded Case Count','Case Submitted Count'])\n",
    "\n",
    "X = new_df[['CaseCount','App Form Downloaded Case Count']] # here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets\n",
    "Y = new_df['Case Submitted Count']\n",
    " \n",
    "# with sklearn\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "\n",
    "# prediction with sklearn\n",
    "New_Case_Count = 5\n",
    "New_App_Form_Downloaded_Case_Count = 12\n",
    "print ('Predicted Case Submitted Count: \\n', regr.predict([[New_Case_Count, New_App_Form_Downloaded_Case_Count]]))\n",
    "\n",
    "# with statsmodels\n",
    "X = sm.add_constant(X) # adding a constant\n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    " \n",
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Intercept: \n 0.221\nCoefficients: \n [-0.081  0.665]\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.DataFrame(df,columns=['Date','DayCreated','CaseCount','App Form Downloaded Case Count','Case Submitted Count']) \n",
    "\n",
    "X = new_df[['CaseCount','App Form Downloaded Case Count']].astype(float) # here we have 2 input variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets. astype is used to convert strings to float in a Python Data Frame. This line converts all X inputs into float values. I am assuming the numbers are strings.\n",
    "#Why am I assuming inputs are string?\n",
    "\n",
    "Y = new_df['Case Submitted Count'].astype(float) # output variable (what we are trying to predict)\n",
    "\n",
    "# with sklearn\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "regression_intercept = np.round (regr.intercept_ , 3)\n",
    "regression_coefficients = np.round (regr.coef_, 3)\n",
    "\n",
    "#Prints interest and coefficients \n",
    "print('Intercept: \\n', regression_intercept) \n",
    "print('Coefficients: \\n', regression_coefficients)\n",
    "\n",
    "# tkinter GUI_Canvas screen created to place items.\n",
    "root= tk.Tk()\n",
    "root.title (\"Predict Applications Submitted\")\n",
    "\n",
    "canvas1 = tk.Canvas(root, width = 500, height = 300)\n",
    "canvas1.pack()\n",
    "\n",
    "\n",
    "# Naming the GUI\n",
    "\n",
    "\n",
    "# New_Interest_Rate label and input box\n",
    "label1 = tk.Label(root, text='Type Number of Applications Created ')\n",
    "canvas1.create_window(110, 80, window=label1) #positioning the label on the canvas\n",
    "\n",
    "entry1 = tk.Entry (root) # create 1st entry box\n",
    "canvas1.create_window(350, 80, window=entry1) #positioning the entry label on the canvas\n",
    "\n",
    "# New_Unemployment_Rate label and input box\n",
    "label2 = tk.Label(root, text=' Type Application forms Downloaded ')\n",
    "canvas1.create_window(105, 120, window=label2) #positioning buttons on the canvas\n",
    "\n",
    "entry2 = tk.Entry (root) # create 2nd entry box\n",
    "canvas1.create_window(350, 120, window=entry2)\n",
    "\n",
    "def values(): \n",
    "    global New_Case_Count #our 1st input variable\n",
    "    New_Case_Count = float(entry1.get()) \n",
    "    \n",
    "    global New_App_Form_Downloaded_Case_Count #our 2nd input variable\n",
    "    New_App_Form_Downloaded_Case_Count = float(entry2.get()) \n",
    "\n",
    "    if (New_Case_Count == 0 or New_App_Form_Downloaded_Case_Count == 0):\n",
    "        Predict_initial = 0\n",
    "\n",
    "    elif (New_Case_Count <= regr.predict([[New_Case_Count, New_App_Form_Downloaded_Case_Count]])):\n",
    "        Predict_initial = New_Case_Count\n",
    "\n",
    "    else:\n",
    "        Predict_initial = int(regr.predict([[New_Case_Count, New_App_Form_Downloaded_Case_Count]]))\n",
    "\n",
    "    Prediction_result  = ('Predicted Number of Applications Submitted: ', Predict_initial)\n",
    "    label_Prediction = tk.Label(root, text= Prediction_result, bg='orange', justify = 'center') #display text on top of canvas\n",
    "    canvas1.create_window(260, 220, window=label_Prediction) #positioning of the orange button on the canvas, just for display\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "button1 = tk.Button (root, text='Predict the Count of Applications Submitted',command=values, bg='orange') # button to call the 'values' command above \n",
    "canvas1.create_window(270, 180, window=button1)\n",
    "\n",
    "# with sklearn\n",
    "Intercept_result = ('Intercept: ', regression_intercept)\n",
    "label_Intercept = tk.Label(root, text=Intercept_result, justify = 'center')\n",
    "canvas1.create_window(260, 280, window=label_Intercept)\n",
    "\n",
    "# with sklearn\n",
    "Coefficients_result  = ('Coefficients: ', regression_coefficients)\n",
    "label_Coefficients = tk.Label(root, text=Coefficients_result, justify = 'center')\n",
    "canvas1.create_window(260, 300, window=label_Coefficients)\n",
    "\n",
    "#plot 1st scatter \n",
    "figure3 = plt.Figure(figsize=(5,4), dpi=100)\n",
    "ax3 = figure3.add_subplot(111)\n",
    "ax3.scatter(df['CaseCount'].astype(float),df['Case Submitted Count'].astype(float), color = 'r')\n",
    "scatter3 = FigureCanvasTkAgg(figure3, root) \n",
    "scatter3.get_tk_widget().pack(side=tk.RIGHT, fill=tk.BOTH)\n",
    "ax3.legend(['Applications Submitted']) \n",
    "ax3.set_xlabel('Applications Created')\n",
    "ax3.set_title('Applications Created Vs. Applications Submitted')\n",
    "\n",
    "#plot 2nd scatter \n",
    "figure4 = plt.Figure(figsize=(5,4), dpi=100)\n",
    "ax4 = figure4.add_subplot(111)\n",
    "ax4.scatter(df['App Form Downloaded Case Count'].astype(float),df['Case Submitted Count'].astype(float), color = 'g')\n",
    "scatter4 = FigureCanvasTkAgg(figure4, root) \n",
    "scatter4.get_tk_widget().pack(side=tk.RIGHT, fill=tk.BOTH)\n",
    "ax4.legend(['Applications Submitted']) \n",
    "ax4.set_xlabel('Application Forms Downloaded')\n",
    "ax4.set_title('Application Forms Downloaded Vs. Applications Submitted')\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}