Need help with this assignment is short% 1. Title: 1984 United States Congressional Voting Records...

Question

Need help with this assignment is short% 1. Title: 1984 United States Congressional Voting Records Database %  % 2. Source Information: %     (a) Source:  Congressional Quarterly Almanac, 98th Congress,  %                  2nd session 1984, Volume XL: Congressional Quarterly Inc.  %                  Washington, D.C., 1985. %     (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) %     (c) Date: 27 April 1987  %  % 3. Past Usage %    - Publications %      1. Schlimmer, J. C. (1987).  Concept acquisition through  %         representational adjustment.  Doctoral dissertation, Department of  %         Information and Computer Science, University of California, Irvine, CA. %         -- Results: about 90%-95% accuracy appears to be STAGGER's asymptote %      - Predicted attribute: party affiliation (2 classes) %  % 4. Relevant Information: %       This data set includes votes for each of the U.S. House of %       Representatives Congressmen on the 16 key votes identified by the %       CQA.  The CQA lists nine different types of votes: voted for, paired %       for, and announced for (these three simplified to yea), voted %       against, paired against, and announced against (these three %       simplified to nay), voted present, voted present to avoid conflict %       of interest, and did not vote or otherwise make a position known %       (these three simplified to an unknown disposition). %  % 5. Number of Instances: 435 (267 democrats, 168 republicans) %  % 6. Number of Attributes: 16 + class name = 17 (all Boolean valued) %  % 7. Attribute Information: %    1. Class Name: 2 (democrat, republican) %    2. handicapped-infants: 2 (y,n) %    3. water-project-cost-sharing: 2 (y,n) %    4. adoption-of-the-budget-resolution: 2 (y,n) %    5. physician-fee-freeze: 2 (y,n) %    6. el-salvador-aid: 2 (y,n) %    7. religious-groups-in-schools: 2 (y,n) %    8. anti-satellite-test-ban: 2 (y,n) %    9. aid-to-nicaraguan-contras: 2 (y,n) %   10. mx-missile: 2 (y,n) %   11. immigration: 2 (y,n) %   12. synfuels-corporation-cutback: 2 (y,n) %   13. education-spending: 2 (y,n) %   14. superfund-right-to-sue: 2 (y,n) %   15. crime: 2 (y,n) %   16. duty-free-exports: 2 (y,n) %   17. export-administration-act-south-africa: 2 (y,n) %  % 8. Missing Attribute Values: Denoted by "?" %  %    NOTE: It is important to recognize that "?" in this database does  %          not mean that the value of the attribute is unknown.  It  %          means simply, that the value is not "yea" or "nay" (see  %          "Relevant Information" section above). %  %    Attribute:  #Missing Values: %            1:  0 %            2:  0 %            3:  12 %            4:  48 %            5:  11 %            6:  11 %            7:  15 %            8:  11 %            9:  14 %           10:  15 %           11:  22 %           12:  7 %           13:  21 %           14:  31 %           15:  25 %           16:  17 %           17:  28 %  % 9. Class Distribution: (2 classes) %    1. 45.2 percent are democrat %    2. 54.8 percent are republican %  % Class predictiveness and predictability: Pr(C|A=V) and Pr(A=V|C) %  Attribute 1: (A = handicapped-infants) %   0.91;  1.21  (C=democrat; V=y) %   0.09;  0.10  (C=republican; V=y) %   0.43;  0.38  (C=democrat; V=n) %   0.57;  0.41  (C=republican; V=n) %   0.75;  0.03  (C=democrat; V=?) %   0.25;  0.01  (C=republican; V=?) %  Attribute 2: (A = water-project-cost-sharing) %   0.62;  0.45  (C=democrat; V=y) %   0.38;  0.23  (C=republican; V=y) %   0.62;  0.45  (C=democrat; V=n) %   0.38;  0.23  (C=republican; V=n) %   0.58;  0.10  (C=democrat; V=?) %   0.42;  0.06  (C=republican; V=?) %  Attribute 3: (A = adoption-of-the-budget-resolution) %   0.91;  0.87  (C=democrat; V=y) %   0.09;  0.07  (C=republican; V=y) %   0.17;  0.11  (C=democrat; V=n) %   0.83;  0.44  (C=republican; V=n) %   0.64;  0.03  (C=democrat; V=?) %   0.36;  0.01  (C=republican; V=?) %  Attribute 4: (A = physician-fee-freeze) %   0.08;  0.05  (C=democrat; V=y) %   0.92;  0.50  (C=republican; V=y) %   0.99;  0.92  (C=democrat; V=n) %   0.01;  0.01  (C=republican; V=n) %   0.73;  0.03  (C=democrat; V=?) %   0.27;  0.01  (C=republican; V=?) %  Attribute 5: (A = el-salvador-aid) %   0.26;  0.21  (C=democrat; V=y) %   0.74;  0.48  (C=republican; V=y) %   0.96;  0.75  (C=democrat; V=n) %   0.04;  0.02  (C=republican; V=n) %   0.80;  0.04  (C=democrat; V=?) %   0.20;  0.01  (C=republican; V=?) %  Attribute 6: (A = religious-groups-in-schools) %   0.45;  0.46  (C=democrat; V=y) %   0.55;  0.46  (C=republican; V=y) %   0.89;  0.51  (C=democrat; V=n) %   0.11;  0.05  (C=republican; V=n) %   0.82;  0.03  (C=democrat; V=?) %   0.18;  0.01  (C=republican; V=?) %  Attribute 7: (A = anti-satellite-test-ban) %   0.84;  0.75  (C=democrat; V=y) %   0.16;  0.12  (C=republican; V=y) %   0.32;  0.22  (C=democrat; V=n) %   0.68;  0.38  (C=republican; V=n) %   0.57;  0.03  (C=democrat; V=?) %   0.43;  0.02  (C=republican; V=?) %  Attribute 8: (A = aid-to-nicaraguan-contras) %   0.90;  0.82  (C=democrat; V=y) %   0.10;  0.07  (C=republican; V=y) %   0.25;  0.17  (C=democrat; V=n) %   0.75;  0.41  (C=republican; V=n) %   0.27;  0.01  (C=democrat; V=?) %   0.73;  0.03  (C=republican; V=?) %  Attribute 9: (A = mx-missile) %   0.91;  0.70  (C=democrat; V=y) %   0.09;  0.06  (C=republican; V=y) %   0.29;  0.22  (C=democrat; V=n) %   0.71;  0.45  (C=republican; V=n) %   0.86;  0.07  (C=democrat; V=?) %   0.14;  0.01  (C=republican; V=?) %  Attribute 10: (A = immigration) %   0.57;  0.46  (C=democrat; V=y) %   0.43;  0.28  (C=republican; V=y) %   0.66;  0.52  (C=democrat; V=n) %   0.34;  0.23  (C=republican; V=n) %   0.57;  0.01  (C=democrat; V=?) %   0.43;  0.01  (C=republican; V=?) %  Attribute 11: (A = synfuels-corporation-cutback) %   0.86;  0.48  (C=democrat; V=y) %   0.14;  0.06  (C=republican; V=y) %   0.48;  0.47  (C=democrat; V=n) %   0.52;  0.43  (C=republican; V=n) %   0.57;  0.04  (C=democrat; V=?) %   0.43;  0.03  (C=republican; V=?) %  Attribute 12: (A = education-spending) %   0.21;  0.13  (C=democrat; V=y) %   0.79;  0.42  (C=republican; V=y) %   0.91;  0.80  (C=democrat; V=n) %   0.09;  0.06  (C=republican; V=n) %   0.58;  0.07  (C=democrat; V=?) %   0.42;  0.04  (C=republican; V=?) %  Attribute 13: (A = superfund-right-to-sue) %   0.35;  0.27  (C=democrat; V=y) %   0.65;  0.42  (C=republican; V=y) %   0.89;  0.67  (C=democrat; V=n) %   0.11;  0.07  (C=republican; V=n) %   0.60;  0.06  (C=democrat; V=?) %   0.40;  0.03  (C=republican; V=?) %  Attribute 14: (A = crime) %   0.36;  0.34  (C=democrat; V=y) %   0.64;  0.49  (C=republican; V=y) %   0.98;  0.63  (C=democrat; V=n) %   0.02;  0.01  (C=republican; V=n) %   0.59;  0.04  (C=democrat; V=?) %   0.41;  0.02  (C=republican; V=?) %  Attribute 15: (A = duty-free-exports)

Apoorv · Accepted Answer

FBLSolution/IONOshphere_dataset.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import arff
",
    "import pandas as pd
",
    "
",
    "
",
    "from sklearn import model_selection 
",
    "from sklearn.ensemble import BaggingClassifier 
",
    "from sklearn.tree import DecisionTreeClassifier 
",
    "
",
    "from sklearn.model_selection import train_test_split
",
    "from sklearn.preprocessing import MultiLabelBinarizer
",
    "from sklearn.preprocessing import LabelEncoder 
",
    "
",
    "import matplotlib.pyplot as plt 
",
    "
",
    "
",
    "
",
    "from scipy.io import arff
",
    "import pandas as pd
",
    "
",
    "
",
    "from sklearn import model_selection 
",
    "from sklearn.ensemble import BaggingClassifier 
",
    "from sklearn.tree import DecisionTreeClassifier 
",
    "
",
    "from sklearn.model_selection import train_test_split
",
    "from sklearn.preprocessing import MultiLabelBinarizer
",
    "
",
    "
",
    "
",
    "from sklearn.metrics import accuracy_score
",
    "
",
    "import matplotlib.pyplot as plt 
",
    "from sklearn import svm, datasets 
",
    "import warnings
",
    "warnings.filterwarnings("ignore")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = arff.loadarff('C:/Users/avira/Downloads/ionosphere-qocpqvmv.arff')
",
    "df = pd.DataFrame(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop("class",axis=1)
",
    "y = df["class"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.get_dummies(df['class'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_definitions_tree_depth(depth):
",
    "    # initialize the base classifier
",
    "    base_cls = DecisionTreeClassifier(max_depth=depth) 
",
    "    # no. of base classifier 
",
    "    # bagging classifier 
",
    "    model = BaggingClassifier(base_estimator = base_cls)
",
    "                          
",
    "    model.fit(X_train,y_train)
",
    "    y_pred = model.predict(X_train)
",
    "    training_accuracy = accuracy_score(y_pred,y_train)
",
    "    y_pred_test = model.predict(X_test)
",
    "    testing_accuracy = accuracy_score(y_pred_test,y_test)
",
    "    
",
    "    return training_accuracy,testing_accuracy
",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_depth_wise(tree_depth):    
",
    "    training = []
",
    "    testing = [] 
",
    "    for depth in tree_depth:
",
    "        tr,te = model_definitions_tree_depth(depth)
",
    "        training.append(tr)
",
    "        testing.append(te)
",
    "    plt.plot(tree_depth, training)
",
    "    plt.plot(tree_depth, testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_depth = [1,2,3,5,10]
",
    "calculate_depth_wise(tree_depth)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here,at depth 2 we are getting most consistent accuracy about 90% of testing and training data but at depth 5 testing accuracy is highest around 95% and training accuracy around 97% ,After this model starts overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_definitions_no_of_trees(number_t):
",
    "    # initialize the base classifier
",
    "    base_cls = DecisionTreeClassifier() 
",
    "    # no. of base classifier 
",
    "    # bagging classifier 
",
    "    model = BaggingClassifier(base_estimator = base_cls,                      
",
    "                          n_estimators = number_t,
",
    "                          )
",
    "    model.fit(X_train,y_train)
",
    "    y_pred = model.predict(X_train)
",
    "    training_accuracy = accuracy_score(y_pred,y_train)
",
    "    y_pred_test = model.predict(X_test)
",
    "    testing_accuracy = accuracy_score(y_pred_test,y_test)
",
    "    
",
    "    return training_accuracy,testing_accuracy
",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_number_tree_wise(num_trees):
",
    "    training = []
",
    "    testing = [] 
",
    "    for trees in num_trees:
",
    "        tr,te = model_definitions_no_of_trees(trees)
",
    "        training.append(tr)
",
    "        testing.append(te)
",
    "    plt.plot(num_trees, training)
",
    "    plt.plot(num_trees, testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_trees = [10,20,40,60,80,100]
",
    "calculate_number_tree_wise(num_trees)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here,when the number of trees are 20-40 the testing accuracy is most of 92% and lowest at 60 number of tress i.e. 88% and the training accuracy is around 1 always."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm_definition_linear(c):
",
    "    model = svm.SVC(kernel ='linear', C = c).fit(X_train,y_train)
",
    "    y_pred = model.predict(X_train)
",
    "    training_accuracy = accuracy_score(y_pred,y_train)
",
    "    y_pred_test = model.predict(X_test)
",
    "    testing_accuracy = accuracy_score(y_pred_test,y_test)
",
    "    
",
    "    return training_accuracy,testing_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_SVM_C_linear(C):
",
    "    training = []
",
    "    testing = [] 
",
    "    for c in C:
",
    "        tr,te = svm_definition_linear(c)
",
    "        training.append(tr)
",
    "        testing.append(te)
",
    "    plt.plot(C, training)
",
    "    plt.plot(C, testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png":

% 1. Title: 1984 United States Congressional Voting Records Database % % 2. Source Information: % (a) Source: Congressional Quarterly Almanac, 98th Congress, % XXXXXXXXXX2nd session 1984, Volume XL:...

Answer To: % 1. Title: 1984 United States Congressional Voting Records Database % % 2. Source Information: %...

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