diff --git a/decision tree classification.ipynb b/decision tree classification.ipynb
index 3ffdabe..ff34977 100644
--- a/decision tree classification.ipynb	
+++ b/decision tree classification.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "scrolled": false
    },
@@ -66,7 +66,7 @@
        "      <td>3.5</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -74,7 +74,7 @@
        "      <td>3.0</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -82,7 +82,7 @@
        "      <td>3.2</td>\n",
        "      <td>1.3</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -90,7 +90,7 @@
        "      <td>3.1</td>\n",
        "      <td>1.5</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -98,7 +98,7 @@
        "      <td>3.6</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -106,7 +106,7 @@
        "      <td>3.9</td>\n",
        "      <td>1.7</td>\n",
        "      <td>0.4</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -114,7 +114,7 @@
        "      <td>3.4</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.3</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -122,7 +122,7 @@
        "      <td>3.4</td>\n",
        "      <td>1.5</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -130,7 +130,7 @@
        "      <td>2.9</td>\n",
        "      <td>1.4</td>\n",
        "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -138,27 +138,27 @@
        "      <td>3.1</td>\n",
        "      <td>1.5</td>\n",
        "      <td>0.1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>Setosa</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width  type\n",
-       "0           5.1          3.5           1.4          0.2     0\n",
-       "1           4.9          3.0           1.4          0.2     0\n",
-       "2           4.7          3.2           1.3          0.2     0\n",
-       "3           4.6          3.1           1.5          0.2     0\n",
-       "4           5.0          3.6           1.4          0.2     0\n",
-       "5           5.4          3.9           1.7          0.4     0\n",
-       "6           4.6          3.4           1.4          0.3     0\n",
-       "7           5.0          3.4           1.5          0.2     0\n",
-       "8           4.4          2.9           1.4          0.2     0\n",
-       "9           4.9          3.1           1.5          0.1     0"
+       "   sepal_length  sepal_width  petal_length  petal_width    type\n",
+       "0           5.1          3.5           1.4          0.2  Setosa\n",
+       "1           4.9          3.0           1.4          0.2  Setosa\n",
+       "2           4.7          3.2           1.3          0.2  Setosa\n",
+       "3           4.6          3.1           1.5          0.2  Setosa\n",
+       "4           5.0          3.6           1.4          0.2  Setosa\n",
+       "5           5.4          3.9           1.7          0.4  Setosa\n",
+       "6           4.6          3.4           1.4          0.3  Setosa\n",
+       "7           5.0          3.4           1.5          0.2  Setosa\n",
+       "8           4.4          2.9           1.4          0.2  Setosa\n",
+       "9           4.9          3.1           1.5          0.1  Setosa"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,6 +221,7 @@
     "        self.min_samples_split = min_samples_split\n",
     "        self.max_depth = max_depth\n",
     "        \n",
+    "        \n",
     "    def build_tree(self, dataset, curr_depth=0):\n",
     "        ''' recursive function to build the tree ''' \n",
     "        \n",
@@ -229,14 +230,17 @@
     "        \n",
     "        # split until stopping conditions are met\n",
     "        if num_samples>=self.min_samples_split and curr_depth<=self.max_depth:\n",
+    "            \n",
     "            # find the best split\n",
-    "            best_split = self.get_best_split(dataset, num_samples, num_features)\n",
+    "            best_split = self.get_best_split(dataset, num_features)\n",
+    "            \n",
     "            # check if information gain is positive\n",
     "            if best_split[\"info_gain\"]>0:\n",
     "                # recur left\n",
     "                left_subtree = self.build_tree(best_split[\"dataset_left\"], curr_depth+1)\n",
     "                # recur right\n",
     "                right_subtree = self.build_tree(best_split[\"dataset_right\"], curr_depth+1)\n",
+    "                \n",
     "                # return decision node\n",
     "                return Node(best_split[\"feature_index\"], best_split[\"threshold\"], \n",
     "                            left_subtree, right_subtree, best_split[\"info_gain\"])\n",
@@ -246,7 +250,8 @@
     "        # return leaf node\n",
     "        return Node(value=leaf_value)\n",
     "    \n",
-    "    def get_best_split(self, dataset, num_samples, num_features):\n",
+    "    \n",
+    "    def get_best_split(self, dataset, num_features):\n",
     "        ''' function to find the best split '''\n",
     "        \n",
     "        # dictionary to store the best split\n",
@@ -257,16 +262,19 @@
     "        for feature_index in range(num_features):\n",
     "            feature_values = dataset[:, feature_index]\n",
     "            possible_thresholds = np.unique(feature_values)\n",
+    "            \n",
     "            # loop over all the feature values present in the data\n",
     "            for threshold in possible_thresholds:\n",
     "                # get current split\n",
     "                dataset_left, dataset_right = self.split(dataset, feature_index, threshold)\n",
+    "                \n",
     "                # check if childs are not null\n",
     "                if len(dataset_left)>0 and len(dataset_right)>0:\n",
     "                    y, left_y, right_y = dataset[:, -1], dataset_left[:, -1], dataset_right[:, -1]\n",
     "                    # compute information gain\n",
     "                    curr_info_gain = self.information_gain(y, left_y, right_y, \"gini\")\n",
-    "                    # update the best split if needed\n",
+    "\n",
+    "                    # update the best split if needed                    \n",
     "                    if curr_info_gain>max_info_gain:\n",
     "                        best_split[\"feature_index\"] = feature_index\n",
     "                        best_split[\"threshold\"] = threshold\n",
@@ -278,6 +286,7 @@
     "        # return best split\n",
     "        return best_split\n",
     "    \n",
+    "    \n",
     "    def split(self, dataset, feature_index, threshold):\n",
     "        ''' function to split the data '''\n",
     "        \n",
@@ -285,17 +294,20 @@
     "        dataset_right = np.array([row for row in dataset if row[feature_index]>threshold])\n",
     "        return dataset_left, dataset_right\n",
     "    \n",
+    "    \n",
     "    def information_gain(self, parent, l_child, r_child, mode=\"entropy\"):\n",
     "        ''' function to compute information gain '''\n",
     "        \n",
     "        weight_l = len(l_child) / len(parent)\n",
     "        weight_r = len(r_child) / len(parent)\n",
+    "        \n",
     "        if mode==\"gini\":\n",
     "            gain = self.gini_index(parent) - (weight_l*self.gini_index(l_child) + weight_r*self.gini_index(r_child))\n",
     "        else:\n",
     "            gain = self.entropy(parent) - (weight_l*self.entropy(l_child) + weight_r*self.entropy(r_child))\n",
     "        return gain\n",
     "    \n",
+    "    \n",
     "    def entropy(self, y):\n",
     "        ''' function to compute entropy '''\n",
     "        \n",
@@ -306,6 +318,7 @@
     "            entropy += -p_cls * np.log2(p_cls)\n",
     "        return entropy\n",
     "    \n",
+    "    \n",
     "    def gini_index(self, y):\n",
     "        ''' function to compute gini index '''\n",
     "        \n",
@@ -316,12 +329,14 @@
     "            gini += p_cls**2\n",
     "        return 1 - gini\n",
     "        \n",
+    "        \n",
     "    def calculate_leaf_value(self, Y):\n",
     "        ''' function to compute leaf node '''\n",
     "        \n",
     "        Y = list(Y)\n",
     "        return max(Y, key=Y.count)\n",
     "    \n",
+    "    \n",
     "    def print_tree(self, tree=None, indent=\" \"):\n",
     "        ''' function to print the tree '''\n",
     "        \n",
@@ -332,17 +347,19 @@
     "            print(tree.value)\n",
     "\n",
     "        else:\n",
-    "            print(\"X_\"+str(tree.feature_index), \"<=\", tree.threshold, \"?\", tree.info_gain)\n",
+    "            print(f'X_{str(tree.feature_index)} <=  {tree.threshold} ? {tree.info_gain}')\n",
     "            print(\"%sleft:\" % (indent), end=\"\")\n",
     "            self.print_tree(tree.left, indent + indent)\n",
     "            print(\"%sright:\" % (indent), end=\"\")\n",
     "            self.print_tree(tree.right, indent + indent)\n",
+    "            \n",
     "    \n",
     "    def fit(self, X, Y):\n",
     "        ''' function to train the tree '''\n",
     "        \n",
     "        dataset = np.concatenate((X, Y), axis=1)\n",
     "        self.root = self.build_tree(dataset)\n",
+    "        \n",
     "    \n",
     "    def predict(self, X):\n",
     "        ''' function to predict new dataset '''\n",
@@ -350,12 +367,13 @@
     "        preditions = [self.make_prediction(x, self.root) for x in X]\n",
     "        return preditions\n",
     "    \n",
+    "    \n",
     "    def make_prediction(self, x, tree):\n",
     "        ''' function to predict a single data point '''\n",
     "        \n",
-    "        if tree.value!=None: return tree.value\n",
+    "        if tree.value is not None: return tree.value\n",
     "        feature_val = x[tree.feature_index]\n",
-    "        if feature_val<=tree.threshold:\n",
+    "        if feature_val <= tree.threshold:\n",
     "            return self.make_prediction(x, tree.left)\n",
     "        else:\n",
     "            return self.make_prediction(x, tree.right)"
@@ -370,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,24 +407,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X_2 <= 1.9 ? 0.33741385372714494\n",
-      " left:0.0\n",
-      " right:X_3 <= 1.5 ? 0.427106638180289\n",
-      "  left:X_2 <= 4.9 ? 0.05124653739612173\n",
-      "    left:1.0\n",
-      "    right:2.0\n",
-      "  right:X_2 <= 5.0 ? 0.019631171921475288\n",
-      "    left:X_1 <= 2.8 ? 0.20833333333333334\n",
-      "        left:2.0\n",
-      "        right:1.0\n",
-      "    right:2.0\n"
+      "X_2 <=  1.9 ? 0.33741385372714494\n",
+      " left:Setosa\n",
+      " right:X_3 <=  1.5 ? 0.427106638180289\n",
+      "  left:X_2 <=  4.9 ? 0.05124653739612173\n",
+      "    left:Versicolor\n",
+      "    right:Virginica\n",
+      "  right:X_2 <=  5.0 ? 0.019631171921475288\n",
+      "    left:X_1 <=  2.8 ? 0.20833333333333334\n",
+      "        left:Virginica\n",
+      "        right:Versicolor\n",
+      "    right:Virginica\n"
      ]
     }
    ],
@@ -425,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +452,7 @@
        "0.9333333333333333"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -462,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/iris.csv b/iris.csv
new file mode 100644
index 0000000..1b9d029
--- /dev/null
+++ b/iris.csv
@@ -0,0 +1,151 @@
+"sepal.length","sepal.width","petal.length","petal.width","variety"
+5.1,3.5,1.4,.2,"Setosa"
+4.9,3,1.4,.2,"Setosa"
+4.7,3.2,1.3,.2,"Setosa"
+4.6,3.1,1.5,.2,"Setosa"
+5,3.6,1.4,.2,"Setosa"
+5.4,3.9,1.7,.4,"Setosa"
+4.6,3.4,1.4,.3,"Setosa"
+5,3.4,1.5,.2,"Setosa"
+4.4,2.9,1.4,.2,"Setosa"
+4.9,3.1,1.5,.1,"Setosa"
+5.4,3.7,1.5,.2,"Setosa"
+4.8,3.4,1.6,.2,"Setosa"
+4.8,3,1.4,.1,"Setosa"
+4.3,3,1.1,.1,"Setosa"
+5.8,4,1.2,.2,"Setosa"
+5.7,4.4,1.5,.4,"Setosa"
+5.4,3.9,1.3,.4,"Setosa"
+5.1,3.5,1.4,.3,"Setosa"
+5.7,3.8,1.7,.3,"Setosa"
+5.1,3.8,1.5,.3,"Setosa"
+5.4,3.4,1.7,.2,"Setosa"
+5.1,3.7,1.5,.4,"Setosa"
+4.6,3.6,1,.2,"Setosa"
+5.1,3.3,1.7,.5,"Setosa"
+4.8,3.4,1.9,.2,"Setosa"
+5,3,1.6,.2,"Setosa"
+5,3.4,1.6,.4,"Setosa"
+5.2,3.5,1.5,.2,"Setosa"
+5.2,3.4,1.4,.2,"Setosa"
+4.7,3.2,1.6,.2,"Setosa"
+4.8,3.1,1.6,.2,"Setosa"
+5.4,3.4,1.5,.4,"Setosa"
+5.2,4.1,1.5,.1,"Setosa"
+5.5,4.2,1.4,.2,"Setosa"
+4.9,3.1,1.5,.2,"Setosa"
+5,3.2,1.2,.2,"Setosa"
+5.5,3.5,1.3,.2,"Setosa"
+4.9,3.6,1.4,.1,"Setosa"
+4.4,3,1.3,.2,"Setosa"
+5.1,3.4,1.5,.2,"Setosa"
+5,3.5,1.3,.3,"Setosa"
+4.5,2.3,1.3,.3,"Setosa"
+4.4,3.2,1.3,.2,"Setosa"
+5,3.5,1.6,.6,"Setosa"
+5.1,3.8,1.9,.4,"Setosa"
+4.8,3,1.4,.3,"Setosa"
+5.1,3.8,1.6,.2,"Setosa"
+4.6,3.2,1.4,.2,"Setosa"
+5.3,3.7,1.5,.2,"Setosa"
+5,3.3,1.4,.2,"Setosa"
+7,3.2,4.7,1.4,"Versicolor"
+6.4,3.2,4.5,1.5,"Versicolor"
+6.9,3.1,4.9,1.5,"Versicolor"
+5.5,2.3,4,1.3,"Versicolor"
+6.5,2.8,4.6,1.5,"Versicolor"
+5.7,2.8,4.5,1.3,"Versicolor"
+6.3,3.3,4.7,1.6,"Versicolor"
+4.9,2.4,3.3,1,"Versicolor"
+6.6,2.9,4.6,1.3,"Versicolor"
+5.2,2.7,3.9,1.4,"Versicolor"
+5,2,3.5,1,"Versicolor"
+5.9,3,4.2,1.5,"Versicolor"
+6,2.2,4,1,"Versicolor"
+6.1,2.9,4.7,1.4,"Versicolor"
+5.6,2.9,3.6,1.3,"Versicolor"
+6.7,3.1,4.4,1.4,"Versicolor"
+5.6,3,4.5,1.5,"Versicolor"
+5.8,2.7,4.1,1,"Versicolor"
+6.2,2.2,4.5,1.5,"Versicolor"
+5.6,2.5,3.9,1.1,"Versicolor"
+5.9,3.2,4.8,1.8,"Versicolor"
+6.1,2.8,4,1.3,"Versicolor"
+6.3,2.5,4.9,1.5,"Versicolor"
+6.1,2.8,4.7,1.2,"Versicolor"
+6.4,2.9,4.3,1.3,"Versicolor"
+6.6,3,4.4,1.4,"Versicolor"
+6.8,2.8,4.8,1.4,"Versicolor"
+6.7,3,5,1.7,"Versicolor"
+6,2.9,4.5,1.5,"Versicolor"
+5.7,2.6,3.5,1,"Versicolor"
+5.5,2.4,3.8,1.1,"Versicolor"
+5.5,2.4,3.7,1,"Versicolor"
+5.8,2.7,3.9,1.2,"Versicolor"
+6,2.7,5.1,1.6,"Versicolor"
+5.4,3,4.5,1.5,"Versicolor"
+6,3.4,4.5,1.6,"Versicolor"
+6.7,3.1,4.7,1.5,"Versicolor"
+6.3,2.3,4.4,1.3,"Versicolor"
+5.6,3,4.1,1.3,"Versicolor"
+5.5,2.5,4,1.3,"Versicolor"
+5.5,2.6,4.4,1.2,"Versicolor"
+6.1,3,4.6,1.4,"Versicolor"
+5.8,2.6,4,1.2,"Versicolor"
+5,2.3,3.3,1,"Versicolor"
+5.6,2.7,4.2,1.3,"Versicolor"
+5.7,3,4.2,1.2,"Versicolor"
+5.7,2.9,4.2,1.3,"Versicolor"
+6.2,2.9,4.3,1.3,"Versicolor"
+5.1,2.5,3,1.1,"Versicolor"
+5.7,2.8,4.1,1.3,"Versicolor"
+6.3,3.3,6,2.5,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+7.1,3,5.9,2.1,"Virginica"
+6.3,2.9,5.6,1.8,"Virginica"
+6.5,3,5.8,2.2,"Virginica"
+7.6,3,6.6,2.1,"Virginica"
+4.9,2.5,4.5,1.7,"Virginica"
+7.3,2.9,6.3,1.8,"Virginica"
+6.7,2.5,5.8,1.8,"Virginica"
+7.2,3.6,6.1,2.5,"Virginica"
+6.5,3.2,5.1,2,"Virginica"
+6.4,2.7,5.3,1.9,"Virginica"
+6.8,3,5.5,2.1,"Virginica"
+5.7,2.5,5,2,"Virginica"
+5.8,2.8,5.1,2.4,"Virginica"
+6.4,3.2,5.3,2.3,"Virginica"
+6.5,3,5.5,1.8,"Virginica"
+7.7,3.8,6.7,2.2,"Virginica"
+7.7,2.6,6.9,2.3,"Virginica"
+6,2.2,5,1.5,"Virginica"
+6.9,3.2,5.7,2.3,"Virginica"
+5.6,2.8,4.9,2,"Virginica"
+7.7,2.8,6.7,2,"Virginica"
+6.3,2.7,4.9,1.8,"Virginica"
+6.7,3.3,5.7,2.1,"Virginica"
+7.2,3.2,6,1.8,"Virginica"
+6.2,2.8,4.8,1.8,"Virginica"
+6.1,3,4.9,1.8,"Virginica"
+6.4,2.8,5.6,2.1,"Virginica"
+7.2,3,5.8,1.6,"Virginica"
+7.4,2.8,6.1,1.9,"Virginica"
+7.9,3.8,6.4,2,"Virginica"
+6.4,2.8,5.6,2.2,"Virginica"
+6.3,2.8,5.1,1.5,"Virginica"
+6.1,2.6,5.6,1.4,"Virginica"
+7.7,3,6.1,2.3,"Virginica"
+6.3,3.4,5.6,2.4,"Virginica"
+6.4,3.1,5.5,1.8,"Virginica"
+6,3,4.8,1.8,"Virginica"
+6.9,3.1,5.4,2.1,"Virginica"
+6.7,3.1,5.6,2.4,"Virginica"
+6.9,3.1,5.1,2.3,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+6.8,3.2,5.9,2.3,"Virginica"
+6.7,3.3,5.7,2.5,"Virginica"
+6.7,3,5.2,2.3,"Virginica"
+6.3,2.5,5,1.9,"Virginica"
+6.5,3,5.2,2,"Virginica"
+6.2,3.4,5.4,2.3,"Virginica"
+5.9,3,5.1,1.8,"Virginica"
\ No newline at end of file