diff --git a/dev/Omobolaji/modules.py b/dev/Omobolaji/modules.py
new file mode 100644
index 000000000..c523257ee
--- /dev/null
+++ b/dev/Omobolaji/modules.py
@@ -0,0 +1,224 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import sklearn
+import seaborn as sns
+
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.metrics import accuracy_score, \
+classification_report, confusion_matrix
+
+# Different models
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import GaussianNB
+
+
+def statistics(data):
+    """View Information on the Dataset"""
+    print("STATISTICS \n")
+    print("Shape of data: ", data.shape , " \n")
+    print("Columns in Data: \n", data.columns, " \n")
+    print("Categories of Target Column: \n", data.Class.unique(), " \n")
+    print("Number of each category in Target Column: \n", data.Class.value_counts(), " \n")
+    print("Total number of null values in each column: \n", data.isnull().sum(), " \n")
+    print("Description of each continuous feature: \n", data.describe().T, " \n")
+    
+def visualization(data):
+    """Graphical Information on the Dataset"""
+    print("An histogram showing categories in Target column, Class \n")
+    sns.countplot(data = data, x= 'Class')
+    plt.show()
+    
+    print("Correlation Analysis of Vehicle dataset \n")
+    fig = plt.gcf()
+    fig.set_size_inches(20, 20)
+    sns.heatmap(data.corr(), annot=True)
+    plt.show()
+    
+    
+def preprocessing(data):
+    """Convert categorical column to continuous"""
+    le = LabelEncoder()
+    data['Class'] = le.fit_transform(data['Class'])
+    """Split the columns into features and target"""
+    features = data.drop(['Class'], axis=1)
+    target = data.Class
+    return train_test_split(features, target, test_size=0.3, random_state=42)
+
+    
+def logistic_regression_model(train_features, test_features, train_target, test_target):
+    """Applying Logistic Regression model on the dataset"""
+    
+    lr = LogisticRegression(solver='liblinear', multi_class='ovr')
+    
+    #fit the training data into the model
+    lr.fit(train_features, train_target)
+    
+    #store the predicted values of test data
+    predict_lr = lr.predict(test_features)
+    
+    #do a cross validation on the model
+    CV = 5
+    cv_results = cross_val_score(lr, train_features, train_target, cv= CV, scoring = 'accuracy')
+    cv = round(cv_results.mean(), 4), round(cv_results.std(), 4) #round values to 4 decimal places
+    print("Cross Validation(mean, standard deviation): ", cv, " \n")
+    
+    #Evaluate accuracy score
+    accuracy = accuracy_score(test_target, predict_lr)
+    print("Accuracy Score(%): ", accuracy*100, " \n")
+    
+    #Compute confusion matrix
+    print("Confusion Matrix \n", confusion_matrix(test_target, predict_lr), " \n") 
+    
+    # Calculate the classification report
+    print("Classification Report \n", classification_report(test_target, predict_lr))
+    
+    
+def k_neighbours_classifier_model(train_features, test_features, train_target, test_target):
+    """Applying KNeigbours Classifier model on the dataset"""
+    
+    knn = KNeighborsClassifier()
+    
+    #fit the training data into the model
+    knn.fit(train_features, train_target)
+    
+    #store the predicted values of test data
+    predict_knn = knn.predict(test_features)
+    
+    #do a cross validation on the model
+    CV = 5
+    cv_results = cross_val_score \
+    (knn, train_features, train_target, cv=CV, scoring = 'accuracy')
+    cv = round(cv_results.mean(), 4), round(cv_results.std(), 4)
+    print("Cross Validation(mean, standard deviation): ", cv, " \n")
+    
+    #Evaluate accuracy score
+    accuracy = accuracy_score(test_target, predict_knn)
+    print("Accuracy Score(%): ", accuracy*100, " \n")
+    
+    #Compute confusion matrix
+    print("Confusion Matrix \n", confusion_matrix(test_target, predict_knn), " \n")
+    
+    # Calculate the classification report
+    print("Classification Report \n", classification_report(test_target, predict_knn))
+    
+    
+    
+def randomforest_classifier_model(train_features, test_features, train_target, test_target):
+    """Applying Random Forest Classifier model on the dataset"""
+    
+    rand = RandomForestClassifier(n_estimators=10, criterion="entropy")
+    
+    #fit the training data into the model
+    rand.fit(train_features, train_target)
+    
+    #store the predicted values of test data
+    predict_rand = rand.predict(test_features)
+    
+    #do a cross validation on the model
+    CV = 5
+    cv_results = cross_val_score(rand, train_features, train_target, cv=CV, scoring = 'accuracy')
+    cv = round(cv_results.mean(), 4), round(cv_results.std(), 4)
+    print("Cross Validation(mean, standard deviation): ", cv, " \n")
+    
+    #Evaluate accuracy score
+    accuracy = accuracy_score(test_target, predict_rand)
+    print("Accuracy Score(%): ", accuracy*100, " \n")
+    
+    #Compute confusion matrix
+    print("Confusion Matrix \n", confusion_matrix(test_target, predict_rand), " \n")
+    
+    # Calculate the classification report
+    print("Classification Report \n", classification_report(test_target, predict_rand))
+    
+    
+
+def decision_tree_classifier_model(train_features, test_features, train_target, test_target):
+    """Applying Decision Tree Classifier model on the dataset"""
+    
+    dtree = DecisionTreeClassifier()
+    
+    #fit the training data into the model
+    dtree.fit(train_features, train_target)
+    
+    #store the predicted values of test data
+    predict_dtree = dtree.predict(test_features)
+    
+    #do a cross validation on the model
+    CV = 5
+    cv_results = cross_val_score(dtree, train_features, train_target, cv=CV, scoring = 'accuracy')
+    cv = round(cv_results.mean(), 4), round(cv_results.std(), 4)
+    print("Cross Validation(mean, standard deviation): ", cv, " \n")
+    
+    #Evaluate accuracy score
+    accuracy = accuracy_score(test_target, predict_dtree)
+    print("Accuracy Score(%): ", accuracy*100, " \n")
+    
+    #Compute confusion matrix
+    print("Confusion Matrix \n", confusion_matrix(test_target, predict_dtree), " \n")
+    
+    # Calculate the classification report
+    print("Classification Report \n", classification_report(test_target, predict_dtree))
+    
+    
+    
+def linear_discriminant_analysis_model(train_features, test_features, train_target, test_target):
+    """Applying Linear Discriminant Analysis model on the dataset"""
+    
+    lda = LinearDiscriminantAnalysis()
+    
+    #fit the training data into the model
+    lda.fit(train_features, train_target)
+    
+    #store the predicted values of test data
+    predict_lda = lda.predict(test_features)
+    
+    #do a cross validation on the model
+    CV = 5
+    cv_results = cross_val_score(lda, train_features, train_target, cv=CV, scoring = 'accuracy')
+    cv = round(cv_results.mean(), 4), round(cv_results.std(), 4)  #calculate the mean and standard deviation, round to 4 decimal places
+    print("Cross Validation(mean, standard deviation): ", cv, " \n")
+    
+    #Evaluate accuracy score
+    accuracy = accuracy_score(test_target, predict_lda)
+    print("Accuracy Score(%): ", accuracy*100, " \n")
+    
+    #Compute confusion matrix
+    print("Confusion Matrix \n", confusion_matrix(test_target, predict_lda), " \n")
+    
+    # Calculate the classification report
+    print("Classification Report \n", classification_report(test_target, predict_lda))
+    
+def gaussian_nb_model(train_features, test_features, train_target, test_target):
+    """Applying Gaussian NB model on the dataset"""
+    
+    nb = GaussianNB()
+    
+    #fit the training data into the model
+    nb.fit(train_features, train_target)
+    
+    #store the predicted values of test data
+    predict_nb = nb.predict(test_features)
+    
+    #do a cross validation on the model
+    CV = 5
+    cv_results = cross_val_score(nb, train_features, train_target, cv=CV, scoring = 'accuracy')
+    cv = round(cv_results.mean(), 4), round(cv_results.std(), 4)
+    print("Cross Validation(mean, standard deviation): ", cv, " \n")
+    
+    #Evaluate accuracy score
+    accuracy = accuracy_score(test_target, predict_nb)
+    print("Accuracy Score(%): ", accuracy*100, " \n")
+    
+    #Compute confusion matrix
+    print("Confusion Matrix \n", confusion_matrix(test_target, predict_nb), " \n")
+    
+    # Calculate the classification report
+    print("Classification Report \n", classification_report(test_target, predict_nb))
+    
\ No newline at end of file
diff --git a/dev/Omobolaji/vehicles.ipynb b/dev/Omobolaji/vehicles.ipynb
new file mode 100644
index 000000000..ddd92a30a
--- /dev/null
+++ b/dev/Omobolaji/vehicles.ipynb
@@ -0,0 +1,1194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### P.S: All libraries and functions used in this notebook are contained in modules.py file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classifying a given Silhouette as one of four types of Vehicle, using a set of features extracted from the Silhouette.\n",
+    "\n",
+    "The purpose of this task is to be able to train a model that can classify a given silhouette as one of the four types of vehicles contained in the dataset. The four model vehicles used for this experiment are namely; a double decker bus (bus), Cheverolet van (van), Saab 9000 (saab) and an Opel Manta 400 (opel). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import modules as mod #import all modules and functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: tabletext in /Users/omobolajibalogun/anaconda3/lib/python3.7/site-packages (0.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#first time run only, install tabletext to be used in generating a table later in this module.\n",
+    "!pip install tabletext"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Accessing the vehicles.csv dataset using its path, variable name assigned is \"vehicles\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vehicles = mod.pd.read_csv(\"./../../datasets/vehicles.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `.head()` prints out the first five rows of the vehicles dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>COMPACTNESS</th>\n",
+       "      <th>CIRCULARITY</th>\n",
+       "      <th>DISTANCE_CIRCULARITY</th>\n",
+       "      <th>RADIUS_RATIO</th>\n",
+       "      <th>PR.AXIS_ASPECT_RATIO</th>\n",
+       "      <th>MAX.LENGTH_ASPECT_RATIO</th>\n",
+       "      <th>SCATTER_RATIO</th>\n",
+       "      <th>ELONGATEDNESS</th>\n",
+       "      <th>PR.AXIS_RECTANGULARITY</th>\n",
+       "      <th>MAX.LENGTH_RECTANGULARITY</th>\n",
+       "      <th>SCALED_VARIANCE_MAJOR</th>\n",
+       "      <th>SCALED_VARIANCE_MINOR</th>\n",
+       "      <th>SCALED_RADIUS_OF_GYRATION</th>\n",
+       "      <th>SKEWNESS_ABOUT_MAJOR</th>\n",
+       "      <th>SKEWNESS_ABOUT_MINOR</th>\n",
+       "      <th>KURTOSIS_ABOUT_MAJOR</th>\n",
+       "      <th>KURTOSIS_ABOUT_MINOR</th>\n",
+       "      <th>HOLLOWS_RATIO</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>95</td>\n",
+       "      <td>48</td>\n",
+       "      <td>83</td>\n",
+       "      <td>178</td>\n",
+       "      <td>72</td>\n",
+       "      <td>10</td>\n",
+       "      <td>162</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20</td>\n",
+       "      <td>159</td>\n",
+       "      <td>176</td>\n",
+       "      <td>379</td>\n",
+       "      <td>184</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>187</td>\n",
+       "      <td>197</td>\n",
+       "      <td>van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>91</td>\n",
+       "      <td>41</td>\n",
+       "      <td>84</td>\n",
+       "      <td>141</td>\n",
+       "      <td>57</td>\n",
+       "      <td>9</td>\n",
+       "      <td>149</td>\n",
+       "      <td>45</td>\n",
+       "      <td>19</td>\n",
+       "      <td>143</td>\n",
+       "      <td>170</td>\n",
+       "      <td>330</td>\n",
+       "      <td>158</td>\n",
+       "      <td>72</td>\n",
+       "      <td>9</td>\n",
+       "      <td>14</td>\n",
+       "      <td>189</td>\n",
+       "      <td>199</td>\n",
+       "      <td>van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>104</td>\n",
+       "      <td>50</td>\n",
+       "      <td>106</td>\n",
+       "      <td>209</td>\n",
+       "      <td>66</td>\n",
+       "      <td>10</td>\n",
+       "      <td>207</td>\n",
+       "      <td>32</td>\n",
+       "      <td>23</td>\n",
+       "      <td>158</td>\n",
+       "      <td>223</td>\n",
+       "      <td>635</td>\n",
+       "      <td>220</td>\n",
+       "      <td>73</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>188</td>\n",
+       "      <td>196</td>\n",
+       "      <td>saab</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>93</td>\n",
+       "      <td>41</td>\n",
+       "      <td>82</td>\n",
+       "      <td>159</td>\n",
+       "      <td>63</td>\n",
+       "      <td>9</td>\n",
+       "      <td>144</td>\n",
+       "      <td>46</td>\n",
+       "      <td>19</td>\n",
+       "      <td>143</td>\n",
+       "      <td>160</td>\n",
+       "      <td>309</td>\n",
+       "      <td>127</td>\n",
+       "      <td>63</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>199</td>\n",
+       "      <td>207</td>\n",
+       "      <td>van</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>85</td>\n",
+       "      <td>44</td>\n",
+       "      <td>70</td>\n",
+       "      <td>205</td>\n",
+       "      <td>103</td>\n",
+       "      <td>52</td>\n",
+       "      <td>149</td>\n",
+       "      <td>45</td>\n",
+       "      <td>19</td>\n",
+       "      <td>144</td>\n",
+       "      <td>241</td>\n",
+       "      <td>325</td>\n",
+       "      <td>188</td>\n",
+       "      <td>127</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>180</td>\n",
+       "      <td>183</td>\n",
+       "      <td>bus</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   COMPACTNESS  CIRCULARITY  DISTANCE_CIRCULARITY  RADIUS_RATIO  \\\n",
+       "0           95           48                    83           178   \n",
+       "1           91           41                    84           141   \n",
+       "2          104           50                   106           209   \n",
+       "3           93           41                    82           159   \n",
+       "4           85           44                    70           205   \n",
+       "\n",
+       "   PR.AXIS_ASPECT_RATIO  MAX.LENGTH_ASPECT_RATIO  SCATTER_RATIO  \\\n",
+       "0                    72                       10            162   \n",
+       "1                    57                        9            149   \n",
+       "2                    66                       10            207   \n",
+       "3                    63                        9            144   \n",
+       "4                   103                       52            149   \n",
+       "\n",
+       "   ELONGATEDNESS  PR.AXIS_RECTANGULARITY  MAX.LENGTH_RECTANGULARITY  \\\n",
+       "0             42                      20                        159   \n",
+       "1             45                      19                        143   \n",
+       "2             32                      23                        158   \n",
+       "3             46                      19                        143   \n",
+       "4             45                      19                        144   \n",
+       "\n",
+       "   SCALED_VARIANCE_MAJOR  SCALED_VARIANCE_MINOR  SCALED_RADIUS_OF_GYRATION  \\\n",
+       "0                    176                    379                        184   \n",
+       "1                    170                    330                        158   \n",
+       "2                    223                    635                        220   \n",
+       "3                    160                    309                        127   \n",
+       "4                    241                    325                        188   \n",
+       "\n",
+       "   SKEWNESS_ABOUT_MAJOR  SKEWNESS_ABOUT_MINOR  KURTOSIS_ABOUT_MAJOR  \\\n",
+       "0                    70                     6                    16   \n",
+       "1                    72                     9                    14   \n",
+       "2                    73                    14                     9   \n",
+       "3                    63                     6                    10   \n",
+       "4                   127                     9                    11   \n",
+       "\n",
+       "   KURTOSIS_ABOUT_MINOR  HOLLOWS_RATIO Class  \n",
+       "0                   187            197   van  \n",
+       "1                   189            199   van  \n",
+       "2                   188            196  saab  \n",
+       "3                   199            207   van  \n",
+       "4                   180            183   bus  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vehicles.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing an overall statistics of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "STATISTICS \n",
+      "\n",
+      "Shape of data:  (846, 19)  \n",
+      "\n",
+      "Columns in Data: \n",
+      " Index(['COMPACTNESS', 'CIRCULARITY', 'DISTANCE_CIRCULARITY', 'RADIUS_RATIO',\n",
+      "       'PR.AXIS_ASPECT_RATIO', 'MAX.LENGTH_ASPECT_RATIO', 'SCATTER_RATIO',\n",
+      "       'ELONGATEDNESS', 'PR.AXIS_RECTANGULARITY', 'MAX.LENGTH_RECTANGULARITY',\n",
+      "       'SCALED_VARIANCE_MAJOR', 'SCALED_VARIANCE_MINOR',\n",
+      "       'SCALED_RADIUS_OF_GYRATION', 'SKEWNESS_ABOUT_MAJOR',\n",
+      "       'SKEWNESS_ABOUT_MINOR', 'KURTOSIS_ABOUT_MAJOR', 'KURTOSIS_ABOUT_MINOR',\n",
+      "       'HOLLOWS_RATIO', 'Class'],\n",
+      "      dtype='object')  \n",
+      "\n",
+      "Categories of Target Column: \n",
+      " ['van' 'saab' 'bus' 'opel']  \n",
+      "\n",
+      "Number of each category in Target Column: \n",
+      " bus     218\n",
+      "saab    217\n",
+      "opel    212\n",
+      "van     199\n",
+      "Name: Class, dtype: int64  \n",
+      "\n",
+      "Total number of null values in each column: \n",
+      " COMPACTNESS                  0\n",
+      "CIRCULARITY                  0\n",
+      "DISTANCE_CIRCULARITY         0\n",
+      "RADIUS_RATIO                 0\n",
+      "PR.AXIS_ASPECT_RATIO         0\n",
+      "MAX.LENGTH_ASPECT_RATIO      0\n",
+      "SCATTER_RATIO                0\n",
+      "ELONGATEDNESS                0\n",
+      "PR.AXIS_RECTANGULARITY       0\n",
+      "MAX.LENGTH_RECTANGULARITY    0\n",
+      "SCALED_VARIANCE_MAJOR        0\n",
+      "SCALED_VARIANCE_MINOR        0\n",
+      "SCALED_RADIUS_OF_GYRATION    0\n",
+      "SKEWNESS_ABOUT_MAJOR         0\n",
+      "SKEWNESS_ABOUT_MINOR         0\n",
+      "KURTOSIS_ABOUT_MAJOR         0\n",
+      "KURTOSIS_ABOUT_MINOR         0\n",
+      "HOLLOWS_RATIO                0\n",
+      "Class                        0\n",
+      "dtype: int64  \n",
+      "\n",
+      "Description of each continuous feature: \n",
+      "                            count        mean         std    min     25%  \\\n",
+      "COMPACTNESS                846.0   93.678487    8.234474   73.0   87.00   \n",
+      "CIRCULARITY                846.0   44.861702    6.169866   33.0   40.00   \n",
+      "DISTANCE_CIRCULARITY       846.0   82.088652   15.771533   40.0   70.00   \n",
+      "RADIUS_RATIO               846.0  168.940898   33.472183  104.0  141.00   \n",
+      "PR.AXIS_ASPECT_RATIO       846.0   61.693853    7.888251   47.0   57.00   \n",
+      "MAX.LENGTH_ASPECT_RATIO    846.0    8.567376    4.601217    2.0    7.00   \n",
+      "SCATTER_RATIO              846.0  168.839243   33.244978  112.0  146.25   \n",
+      "ELONGATEDNESS              846.0   40.933806    7.811560   26.0   33.00   \n",
+      "PR.AXIS_RECTANGULARITY     846.0   20.582742    2.592138   17.0   19.00   \n",
+      "MAX.LENGTH_RECTANGULARITY  846.0  147.998818   14.515652  118.0  137.00   \n",
+      "SCALED_VARIANCE_MAJOR      846.0  188.625296   31.394837  130.0  167.00   \n",
+      "SCALED_VARIANCE_MINOR      846.0  439.911348  176.692614  184.0  318.25   \n",
+      "SCALED_RADIUS_OF_GYRATION  846.0  174.703310   32.546490  109.0  149.00   \n",
+      "SKEWNESS_ABOUT_MAJOR       846.0   72.462175    7.486974   59.0   67.00   \n",
+      "SKEWNESS_ABOUT_MINOR       846.0    6.377069    4.918353    0.0    2.00   \n",
+      "KURTOSIS_ABOUT_MAJOR       846.0   12.599291    8.931240    0.0    5.00   \n",
+      "KURTOSIS_ABOUT_MINOR       846.0  188.932624    6.163949  176.0  184.00   \n",
+      "HOLLOWS_RATIO              846.0  195.632388    7.438797  181.0  190.25   \n",
+      "\n",
+      "                             50%    75%     max  \n",
+      "COMPACTNESS                 93.0  100.0   119.0  \n",
+      "CIRCULARITY                 44.0   49.0    59.0  \n",
+      "DISTANCE_CIRCULARITY        80.0   98.0   112.0  \n",
+      "RADIUS_RATIO               167.0  195.0   333.0  \n",
+      "PR.AXIS_ASPECT_RATIO        61.0   65.0   138.0  \n",
+      "MAX.LENGTH_ASPECT_RATIO      8.0   10.0    55.0  \n",
+      "SCATTER_RATIO              157.0  198.0   265.0  \n",
+      "ELONGATEDNESS               43.0   46.0    61.0  \n",
+      "PR.AXIS_RECTANGULARITY      20.0   23.0    29.0  \n",
+      "MAX.LENGTH_RECTANGULARITY  146.0  159.0   188.0  \n",
+      "SCALED_VARIANCE_MAJOR      178.5  217.0   320.0  \n",
+      "SCALED_VARIANCE_MINOR      364.0  587.0  1018.0  \n",
+      "SCALED_RADIUS_OF_GYRATION  173.0  198.0   268.0  \n",
+      "SKEWNESS_ABOUT_MAJOR        71.5   75.0   135.0  \n",
+      "SKEWNESS_ABOUT_MINOR         6.0    9.0    22.0  \n",
+      "KURTOSIS_ABOUT_MAJOR        11.0   19.0    41.0  \n",
+      "KURTOSIS_ABOUT_MINOR       188.0  193.0   206.0  \n",
+      "HOLLOWS_RATIO              197.0  201.0   211.0    \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mod.statistics(vehicles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Meanings of all the features\n",
+    "\n",
+    "COMPACTNESS (average perim)**2/area\n",
+    "\n",
+    "CIRCULARITY (average radius)**2/area\n",
+    "\n",
+    "DISTANCE CIRCULARITY area/(av.distance from border)**2\n",
+    "\n",
+    "RADIUS RATIO (max.rad-min.rad)/av.radius\n",
+    "\n",
+    "PR.AXIS ASPECT RATIO (minor axis)/(major axis)\n",
+    "\n",
+    "MAX.LENGTH ASPECT RATIO (length perp. max length)/(max length)\n",
+    "\n",
+    "SCATTER RATIO (inertia about minor axis)/(inertia about major axis)\n",
+    "\n",
+    "ELONGATEDNESS area/(shrink width)**2\n",
+    "\n",
+    "PR.AXIS RECTANGULARITY area/(pr.axis length*pr.axis width)\n",
+    "\n",
+    "MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this)\n",
+    "\n",
+    "SCALED VARIANCE (2nd order moment about minor axis)/area\n",
+    "ALONG MAJOR AXIS\n",
+    "\n",
+    "SCALED VARIANCE (2nd order moment about major axis)/area\n",
+    "ALONG MINOR AXIS\n",
+    "\n",
+    "SCALED RADIUS OF GYRATION (mavar+mivar)/area\n",
+    "\n",
+    "SKEWNESS ABOUT (3rd order moment about major axis)/sigma_min**3\n",
+    "MAJOR AXIS\n",
+    "\n",
+    "SKEWNESS ABOUT (3rd order moment about minor axis)/sigma_maj**3\n",
+    "MINOR AXIS\n",
+    "\n",
+    "KURTOSIS ABOUT (4th order moment about major axis)/sigma_min**4\n",
+    "MINOR AXIS\n",
+    "\n",
+    "KURTOSIS ABOUT (4th order moment about minor axis)/sigma_maj**4\n",
+    "MAJOR AXIS\n",
+    "\n",
+    "HOLLOWS RATIO (area of hollows)/(area of bounding polygon)\n",
+    "\n",
+    "Where sigma_maj**2 is the variance along the major axis and sigma_min**2 is the variance along the minor axis, and\n",
+    "\n",
+    "area of hollows= area of bounding poly-area of object\n",
+    "\n",
+    "The area of the bounding polygon is found as a side result of the computation to find the maximum length. Each individual length computation yields a pair of calipers to the object orientated at every 5 degrees. The object is propagated into an image containing the union of these calipers to obtain an image of the bounding polygon.\n",
+    "\n",
+    "\n",
+    "Source: https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference from the above\n",
+    "\n",
+    "There are 18 features that were observed to be possessed by each vehicle, the last column (Class) displays the type of vehicle associated with the dimensions of each feature. No null value is present in the dataset.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "An histogram showing categories in Target column, Class \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Analysis of Vehicle dataset \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mod.visualization(vehicles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The classes of vehicles in the 846 rows of data are \"bus, van, saab and opel\", with \"van\" having the least number of observations and quite a larger difference compared to the other three vehicles while \"bus\" has the highest occurence observed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to predict the category of a vehicle given the dimensions of its features, we need to train a model that will give the highest possible accuracy based on this given data. This is a supervised learning problem, because we have labeled data. There are a lots of supervised learning algorithms, the question now is which algorithm will be fit for this task.\n",
+    "\n",
+    "First, it is good to be able to identify the kind of data in the target column, that is, \"Class\". We are predicting a category of vehicles and not some quantity nor continuous data, this shows we are dealing with a Classification problem and not Regression (regression deals with continuous data/ non-categorical data).\n",
+    "\n",
+    "Further, there are still several Classification algorithms, some of which are: Logistic Regression, Naive Bayes, KNeighbour Classifier, Support Vector Machine, Random Forest, Decision Trees, SGDClassifier, et ce tera.\n",
+    "\n",
+    "In this notebook, I will test out some of these algorithms and compare their evaluation metrics to one another in order to determine the best algorithm for this dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splitting the columns of the dataset into features and target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Something common to building all these models is splitting our dataset into train set and test set. Train set, being a larger part of the data in which I will train the model on and test set, being a part of the data used to test the built model on as to determine its accuracy/evaluation metrics.\n",
+    "\n",
+    "Sckit-learn has a `train_test_split` function in its `model_selection` package, this is what I will use to split the data.\n",
+    "I will split the train and test data further into train_features, train_target and test_features, test_target.\n",
+    "\n",
+    "`train_features` a fraction of all explanatory variables to be used in training the model\n",
+    "\n",
+    "`train_target` a fraction of the target variable to be used in training the model\n",
+    "\n",
+    "`test_features` a fraction of all explanatory variables used for testing the model's performance\n",
+    "\n",
+    "`test_target` a part of the target variable used for testing the model's performance.\n",
+    "\n",
+    "\n",
+    "The next challenge is determining what `test_size` to use in splitting the data. I have to be careful when choosing this so I won't end up overfitting/underfitting the data into a model. The train_set should be larger than the test_set, this is because a model performs better when trained on a large amount of data, therefore the test_size should naturally not be more than 0.4/40% of the data. This leaves me with a decision to choose between test_size of 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1. \n",
+    "\n",
+    "Next, I will try out these different test_sizes on some models and pick out the one with the highest model accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "┌────────────────────────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┐\n",
+      "│ test_size/model        │  0.4 │ 0.35 │  0.3 │ 0.25 │  0.2 │ 0.15 │  0.1 │\n",
+      "├────────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┼──────┤\n",
+      "│ Logistic_regression(%) │ 80.2 │ 81.8 │ 80.7 │ 81.1 │ 81.2 │ 77.2 │ 77.6 │\n",
+      "├────────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┼──────┤\n",
+      "│ KNeighbours(%)         │ 61.1 │ 62.6 │ 63.4 │ 63.2 │ 64.7 │ 64.6 │ 63.5 │\n",
+      "├────────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┼──────┤\n",
+      "│ RandomForest(%)        │ 71.1 │ 77.4 │ 76.8 │ 76.4 │ 75.3 │ 81.1 │ 72.9 │\n",
+      "├────────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┼──────┤\n",
+      "│ Decision Tree(%)       │ 74.3 │ 71.0 │ 76.0 │ 68.9 │ 72.4 │ 70.1 │ 74.1 │\n",
+      "├────────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┼──────┤\n",
+      "│ Linear Discriminant(%) │ 78.8 │ 79.5 │ 79.5 │ 79.7 │ 77.1 │ 76.4 │ 77.6 │\n",
+      "├────────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┼──────┤\n",
+      "│ Gaussian NB(%)         │ 41.6 │ 44.1 │ 45.7 │ 48.6 │ 50.0 │ 53.5 │ 49.4 │\n",
+      "└────────────────────────┴──────┴──────┴──────┴──────┴──────┴──────┴──────┘\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Results of the various model performance on different test_sizes\n",
+    "import tabletext\n",
+    "model_performance = [\n",
+    "                    ['test_size/model', 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1], \n",
+    "                    ['Logistic_regression(%)', 80.2, 81.8, 80.7, 81.1, 81.2, 77.2, 77.6],\n",
+    "                    ['KNeighbours(%)', 61.1, 62.6, 63.4, 63.2, 64.7, 64.6, 63.5],\n",
+    "                    ['RandomForest(%)', 71.1, 77.4, 76.8, 76.4, 75.3, 81.1, 72.9],\n",
+    "                    ['Decision Tree(%)', 74.3, 71.0, 76.0, 68.9, 72.4, 70.1, 74.1],\n",
+    "                    ['Linear Discriminant(%)', 78.8, 79.5, 79.5, 79.7, 77.1, 76.4, 77.6],\n",
+    "                    ['Gaussian NB(%)', 41.6, 44.1, 45.7, 48.6, 50.0, 53.5, 49.4]\n",
+    "                    ]\n",
+    "print(tabletext.to_text(model_performance))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inference\n",
+    "The first row above shows the various test sizes I tried, the first column indicates the names of the different models used, the rest are a percentage of accuracy score corresponding to a test_size and a model. \n",
+    "\n",
+    "Careful observation shows that two models (Decision Tree & Linear Discriminant) are best at `test_size` of 0.3, Logistic Regression is best at 0.35 as `test_size`, KNeighbours is best at `test_size` of 0.2 with slight difference among the other test sizes while RandomForest is best at a `test_size` of 0.15 (81.1%) followed by a `test_size` of 0.35 (77.4%). \n",
+    "\n",
+    "Gaussian NB has a normally low accuracy, it is known to be a bad estimator and performs better on a small amount of training data, therefore I will exclude it from the models to be used.\n",
+    "\n",
+    "The model with the an overall highest accuracy is Logistic Regression (81.8%), followed by RandomForest (81.1%), Linear Discriminant Analysis (79.7%), Decision Tree (76.0%), KNeighbours (64.7%) and the least being Gaussian NB (53.5%).\n",
+    "\n",
+    "\n",
+    "### Final Inference on test_size\n",
+    "I will choose my `test_size` as **0.3** as it seems to be the overall best among the others and will give a good performance of the models.\n",
+    "\n",
+    "### Conclusion\n",
+    "I will focus more on tuning the paramters of the Logistic Regression model and Random Forest Classifier in order to achieve the highest possible accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features, test_features, train_target, test_target = mod.preprocessing(vehicles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In mod.preprocessing function, I have used LabelEncoder() to convert the categorical vehicle classes in the target \"Class\" column to model-understandable numerical values. This is because a model can easily interpret numerical data when compared to text-like data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>COMPACTNESS</th>\n",
+       "      <th>CIRCULARITY</th>\n",
+       "      <th>DISTANCE_CIRCULARITY</th>\n",
+       "      <th>RADIUS_RATIO</th>\n",
+       "      <th>PR.AXIS_ASPECT_RATIO</th>\n",
+       "      <th>MAX.LENGTH_ASPECT_RATIO</th>\n",
+       "      <th>SCATTER_RATIO</th>\n",
+       "      <th>ELONGATEDNESS</th>\n",
+       "      <th>PR.AXIS_RECTANGULARITY</th>\n",
+       "      <th>MAX.LENGTH_RECTANGULARITY</th>\n",
+       "      <th>SCALED_VARIANCE_MAJOR</th>\n",
+       "      <th>SCALED_VARIANCE_MINOR</th>\n",
+       "      <th>SCALED_RADIUS_OF_GYRATION</th>\n",
+       "      <th>SKEWNESS_ABOUT_MAJOR</th>\n",
+       "      <th>SKEWNESS_ABOUT_MINOR</th>\n",
+       "      <th>KURTOSIS_ABOUT_MAJOR</th>\n",
+       "      <th>KURTOSIS_ABOUT_MINOR</th>\n",
+       "      <th>HOLLOWS_RATIO</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>95</td>\n",
+       "      <td>48</td>\n",
+       "      <td>83</td>\n",
+       "      <td>178</td>\n",
+       "      <td>72</td>\n",
+       "      <td>10</td>\n",
+       "      <td>162</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20</td>\n",
+       "      <td>159</td>\n",
+       "      <td>176</td>\n",
+       "      <td>379</td>\n",
+       "      <td>184</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>187</td>\n",
+       "      <td>197</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>91</td>\n",
+       "      <td>41</td>\n",
+       "      <td>84</td>\n",
+       "      <td>141</td>\n",
+       "      <td>57</td>\n",
+       "      <td>9</td>\n",
+       "      <td>149</td>\n",
+       "      <td>45</td>\n",
+       "      <td>19</td>\n",
+       "      <td>143</td>\n",
+       "      <td>170</td>\n",
+       "      <td>330</td>\n",
+       "      <td>158</td>\n",
+       "      <td>72</td>\n",
+       "      <td>9</td>\n",
+       "      <td>14</td>\n",
+       "      <td>189</td>\n",
+       "      <td>199</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>104</td>\n",
+       "      <td>50</td>\n",
+       "      <td>106</td>\n",
+       "      <td>209</td>\n",
+       "      <td>66</td>\n",
+       "      <td>10</td>\n",
+       "      <td>207</td>\n",
+       "      <td>32</td>\n",
+       "      <td>23</td>\n",
+       "      <td>158</td>\n",
+       "      <td>223</td>\n",
+       "      <td>635</td>\n",
+       "      <td>220</td>\n",
+       "      <td>73</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>188</td>\n",
+       "      <td>196</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>93</td>\n",
+       "      <td>41</td>\n",
+       "      <td>82</td>\n",
+       "      <td>159</td>\n",
+       "      <td>63</td>\n",
+       "      <td>9</td>\n",
+       "      <td>144</td>\n",
+       "      <td>46</td>\n",
+       "      <td>19</td>\n",
+       "      <td>143</td>\n",
+       "      <td>160</td>\n",
+       "      <td>309</td>\n",
+       "      <td>127</td>\n",
+       "      <td>63</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>199</td>\n",
+       "      <td>207</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>85</td>\n",
+       "      <td>44</td>\n",
+       "      <td>70</td>\n",
+       "      <td>205</td>\n",
+       "      <td>103</td>\n",
+       "      <td>52</td>\n",
+       "      <td>149</td>\n",
+       "      <td>45</td>\n",
+       "      <td>19</td>\n",
+       "      <td>144</td>\n",
+       "      <td>241</td>\n",
+       "      <td>325</td>\n",
+       "      <td>188</td>\n",
+       "      <td>127</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>180</td>\n",
+       "      <td>183</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   COMPACTNESS  CIRCULARITY  DISTANCE_CIRCULARITY  RADIUS_RATIO  \\\n",
+       "0           95           48                    83           178   \n",
+       "1           91           41                    84           141   \n",
+       "2          104           50                   106           209   \n",
+       "3           93           41                    82           159   \n",
+       "4           85           44                    70           205   \n",
+       "\n",
+       "   PR.AXIS_ASPECT_RATIO  MAX.LENGTH_ASPECT_RATIO  SCATTER_RATIO  \\\n",
+       "0                    72                       10            162   \n",
+       "1                    57                        9            149   \n",
+       "2                    66                       10            207   \n",
+       "3                    63                        9            144   \n",
+       "4                   103                       52            149   \n",
+       "\n",
+       "   ELONGATEDNESS  PR.AXIS_RECTANGULARITY  MAX.LENGTH_RECTANGULARITY  \\\n",
+       "0             42                      20                        159   \n",
+       "1             45                      19                        143   \n",
+       "2             32                      23                        158   \n",
+       "3             46                      19                        143   \n",
+       "4             45                      19                        144   \n",
+       "\n",
+       "   SCALED_VARIANCE_MAJOR  SCALED_VARIANCE_MINOR  SCALED_RADIUS_OF_GYRATION  \\\n",
+       "0                    176                    379                        184   \n",
+       "1                    170                    330                        158   \n",
+       "2                    223                    635                        220   \n",
+       "3                    160                    309                        127   \n",
+       "4                    241                    325                        188   \n",
+       "\n",
+       "   SKEWNESS_ABOUT_MAJOR  SKEWNESS_ABOUT_MINOR  KURTOSIS_ABOUT_MAJOR  \\\n",
+       "0                    70                     6                    16   \n",
+       "1                    72                     9                    14   \n",
+       "2                    73                    14                     9   \n",
+       "3                    63                     6                    10   \n",
+       "4                   127                     9                    11   \n",
+       "\n",
+       "   KURTOSIS_ABOUT_MINOR  HOLLOWS_RATIO  Class  \n",
+       "0                   187            197      3  \n",
+       "1                   189            199      3  \n",
+       "2                   188            196      2  \n",
+       "3                   199            207      3  \n",
+       "4                   180            183      0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vehicles.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From our new labels, we can see that:\n",
+    "Vehicle class **opel** was assigned the label **0**\n",
+    "\n",
+    "Vehicle class **bus** was assigned the label **1**\n",
+    "\n",
+    "Vehicle class **saab** was assigned the label **2**\n",
+    "\n",
+    "Vehicle class **van** was assigned the label **3**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating the Performance of different Classification models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explaining Keywords used in Model Performances\n",
+    "\n",
+    "- Accuracy(%): A percentage of correctly predicted observations to the total observations\n",
+    "- Precision: How often is the prediction of a positive value correct?\n",
+    "- Recall: How often is the prediction of a True Positive correct?\n",
+    "- F1-Score: The weighted average of Precision and Recall."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation(mean, standard deviation):  (0.7904, 0.0338)  \n",
+      "\n",
+      "Accuracy Score(%):  80.70866141732283  \n",
+      "\n",
+      "Confusion Matrix \n",
+      " [[77  0  0  1]\n",
+      " [ 2 32 12  1]\n",
+      " [ 4 26 40  1]\n",
+      " [ 0  2  0 56]]  \n",
+      "\n",
+      "Classification Report \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.93      0.99      0.96        78\n",
+      "           1       0.53      0.68      0.60        47\n",
+      "           2       0.77      0.56      0.65        71\n",
+      "           3       0.95      0.97      0.96        58\n",
+      "\n",
+      "    accuracy                           0.81       254\n",
+      "   macro avg       0.79      0.80      0.79       254\n",
+      "weighted avg       0.82      0.81      0.80       254\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Logistic Regression model\n",
+    "mod.logistic_regression_model(train_features, test_features, train_target, test_target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inference from the the Classification Report using Logistic Regression model\n",
+    "Vehicle class 0 (opel) has been predicted with:\n",
+    "- 0.93 precision, 93% of the predictions are correct\n",
+    "- 0.99 precision, 99% of positive cases of opel class were caught\n",
+    "- 0.96 f1-score, 96% of the positive predictions were correct\n",
+    "\n",
+    "Vehicle class 1 (bus) has been predicted with:\n",
+    "- 0.53 precision, 53% of the predictions are correct\n",
+    "- 0.68 precision, 68% of positive cases of bus class were caught\n",
+    "- 0.60 f1-score, 60% of the positive predictions were correct\n",
+    "\n",
+    "Vehicle class 2 (saab) has been predicted with:\n",
+    "- 0.77 precision, 77% of the predictions are correct\n",
+    "- 0.56 precision, 56% of positive cases of saab class were caught\n",
+    "- 0.65 f1-score, 65% of the positive predictions were correct\n",
+    "\n",
+    "Vehicle class 3 (van) has been predicted with:\n",
+    "- 0.95 precision, 95% of the predictions are correct\n",
+    "- 0.97 precision, 97% of positive cases of van class were caught\n",
+    "- 0.96 f1-score, 96% of the positive predictions were correct\n",
+    "\n",
+    "**Support** column in this classification report lists the number of samples for each class (78 samples for opel, 47 samples for bus, 71 samples for saab, 58 samples for van)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation(mean, standard deviation):  (0.6522, 0.0458)  \n",
+      "\n",
+      "Accuracy Score(%):  63.38582677165354  \n",
+      "\n",
+      "Confusion Matrix \n",
+      " [[63  4 10  1]\n",
+      " [ 0 20 23  4]\n",
+      " [ 8 32 28  3]\n",
+      " [ 3  3  2 50]]  \n",
+      "\n",
+      "Classification Report \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.85      0.81      0.83        78\n",
+      "           1       0.34      0.43      0.38        47\n",
+      "           2       0.44      0.39      0.42        71\n",
+      "           3       0.86      0.86      0.86        58\n",
+      "\n",
+      "    accuracy                           0.63       254\n",
+      "   macro avg       0.62      0.62      0.62       254\n",
+      "weighted avg       0.65      0.63      0.64       254\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Kneighbours classifier model\n",
+    "mod.k_neighbours_classifier_model(train_features, test_features, train_target, test_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation(mean, standard deviation):  (0.711, 0.0235)  \n",
+      "\n",
+      "Accuracy Score(%):  74.01574803149606  \n",
+      "\n",
+      "Confusion Matrix \n",
+      " [[75  0  3  0]\n",
+      " [ 0 26 20  1]\n",
+      " [ 3 30 34  4]\n",
+      " [ 1  0  4 53]]  \n",
+      "\n",
+      "Classification Report \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.95      0.96      0.96        78\n",
+      "           1       0.46      0.55      0.50        47\n",
+      "           2       0.56      0.48      0.52        71\n",
+      "           3       0.91      0.91      0.91        58\n",
+      "\n",
+      "    accuracy                           0.74       254\n",
+      "   macro avg       0.72      0.73      0.72       254\n",
+      "weighted avg       0.74      0.74      0.74       254\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Random Forest classifier model\n",
+    "mod.randomforest_classifier_model(train_features, test_features, train_target, test_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation(mean, standard deviation):  (0.6858, 0.0164)  \n",
+      "\n",
+      "Accuracy Score(%):  75.59055118110236  \n",
+      "\n",
+      "Confusion Matrix \n",
+      " [[75  1  2  0]\n",
+      " [ 2 29 16  0]\n",
+      " [ 7 24 39  1]\n",
+      " [ 0  5  4 49]]  \n",
+      "\n",
+      "Classification Report \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.89      0.96      0.93        78\n",
+      "           1       0.49      0.62      0.55        47\n",
+      "           2       0.64      0.55      0.59        71\n",
+      "           3       0.98      0.84      0.91        58\n",
+      "\n",
+      "    accuracy                           0.76       254\n",
+      "   macro avg       0.75      0.74      0.74       254\n",
+      "weighted avg       0.77      0.76      0.76       254\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Decision Tree Classifier model\n",
+    "mod.decision_tree_classifier_model(train_features, test_features, train_target, test_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation(mean, standard deviation):  (0.7854, 0.029)  \n",
+      "\n",
+      "Accuracy Score(%):  79.52755905511812  \n",
+      "\n",
+      "Confusion Matrix \n",
+      " [[76  0  0  2]\n",
+      " [ 2 33 11  1]\n",
+      " [ 6 27 36  2]\n",
+      " [ 0  0  1 57]]  \n",
+      "\n",
+      "Classification Report \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.90      0.97      0.94        78\n",
+      "           1       0.55      0.70      0.62        47\n",
+      "           2       0.75      0.51      0.61        71\n",
+      "           3       0.92      0.98      0.95        58\n",
+      "\n",
+      "    accuracy                           0.80       254\n",
+      "   macro avg       0.78      0.79      0.78       254\n",
+      "weighted avg       0.80      0.80      0.79       254\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Linear Discriminant Analysis\n",
+    "mod.linear_discriminant_analysis_model(train_features, test_features, train_target, test_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation(mean, standard deviation):  (0.4646, 0.0405)  \n",
+      "\n",
+      "Accuracy Score(%):  45.66929133858268  \n",
+      "\n",
+      "Confusion Matrix \n",
+      " [[ 9 25  2 42]\n",
+      " [ 0 20 15 12]\n",
+      " [ 2 17 31 21]\n",
+      " [ 1  1  0 56]]  \n",
+      "\n",
+      "Classification Report \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.75      0.12      0.20        78\n",
+      "           1       0.32      0.43      0.36        47\n",
+      "           2       0.65      0.44      0.52        71\n",
+      "           3       0.43      0.97      0.59        58\n",
+      "\n",
+      "    accuracy                           0.46       254\n",
+      "   macro avg       0.54      0.49      0.42       254\n",
+      "weighted avg       0.57      0.46      0.41       254\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Gaussian NB model\n",
+    "mod.gaussian_nb_model(train_features, test_features, train_target, test_target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Tune the parameters of Logistic Regression model and Random Forest Classifier model to achieve a better performance model.\n"
+   ]
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}