diff --git a/TestBayes.ipynb b/TestBayes.ipynb
deleted file mode 100644
index 497667d..0000000
--- a/TestBayes.ipynb
+++ /dev/null
@@ -1,182 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np \n",
-    "import pandas as pd \n",
-    "import matplotlib.pyplot as plt \n",
-    "import math\n",
-    "\n",
-    "\n",
-    "def accuracy_score(y_true, y_pred):\n",
-    "\n",
-    "    \"\"\"\tscore = (y_true - y_pred) / len(y_true) \"\"\"\n",
-    "\n",
-    "    return round(float(sum(y_pred == y_true))/float(len(y_true)) * 100 ,2)\n",
-    "\n",
-    "def pre_processing(df):\n",
-    "\n",
-    "    \"\"\" partioning data into features and target \"\"\"\n",
-    "\n",
-    "    X = df.drop([df.columns[-1]], axis = 1)\n",
-    "    y = df[df.columns[-1]]\n",
-    "\n",
-    "    return X, y\n",
-    "\n",
-    "\n",
-    "\n",
-    "class  NaiveBayes:\n",
-    "\n",
-    "def __init__(self):\n",
-    "\n",
-    "        self.features = list\n",
-    "        self.likelihoods = {}\n",
-    "        self.class_priors = {}\n",
-    "        self.pred_priors = {}\n",
-    "\n",
-    "        self.X_train = np.array\n",
-    "        self.y_train = np.array\n",
-    "        self.train_size = int\n",
-    "        self.num_feats = int\n",
-    "\n",
-    "\tdef fit(self, X, y):\n",
-    "\n",
-    "        self.features = list(X.columns)\n",
-    "        self.X_train = X\n",
-    "        self.y_train = y\n",
-    "        self.train_size = X.shape[0]\n",
-    "        self.num_feats = X.shape[1]\n",
-    "\n",
-    "        for feature in self.features:\n",
-    "            self.likelihoods[feature] = {}\n",
-    "            self.pred_priors[feature] = {}\n",
-    "\n",
-    "            for feat_val in np.unique(self.X_train[feature]):\n",
-    "                self.pred_priors[feature].update({feat_val: 0})\n",
-    "\n",
-    "                for outcome in np.unique(self.y_train):\n",
-    "                    self.likelihoods[feature].update({feat_val+'_'+outcome:0})\n",
-    "                    self.class_priors.update({outcome: 0})\n",
-    "\n",
-    "        self._calc_class_prior()\n",
-    "        self._calc_likelihoods()\n",
-    "        self._calc_predictor_prior()\n",
-    "\n",
-    "    def _calc_class_prior(self):\n",
-    "\n",
-    "    \"\"\" P(c) - Prior Class Probability \"\"\"\n",
-    "\n",
-    "        for outcome in np.unique(self.y_train):\n",
-    "            outcome_count = sum(self.y_train == outcome)\n",
-    "            self.class_priors[outcome] = outcome_count / self.train_size\n",
-    "\n",
-    "    def _calc_likelihoods(self):\n",
-    "\n",
-    "        \"\"\" P(x|c) - Likelihood \"\"\"\n",
-    "\n",
-    "        for feature in self.features:\n",
-    "\n",
-    "            for outcome in np.unique(self.y_train):\n",
-    "                outcome_count = sum(self.y_train == outcome)\n",
-    "                feat_likelihood = self.X_train[feature][self.y_train[self.y_train == outcome].index.values.tolist()].value_counts().to_dict()\n",
-    "\n",
-    "                for feat_val, count in feat_likelihood.items():\n",
-    "                    self.likelihoods[feature][feat_val + '_' + outcome] = count/outcome_count\n",
-    "\n",
-    "\n",
-    "    def _calc_predictor_prior(self):\n",
-    "\n",
-    "        \"\"\" P(x) - Evidence \"\"\"\n",
-    "\n",
-    "        for feature in self.features:\n",
-    "            feat_vals = self.X_train[feature].value_counts().to_dict()\n",
-    "\n",
-    "            for feat_val, count in feat_vals.items():\n",
-    "                self.pred_priors[feature][feat_val] = count/self.train_size\n",
-    "\n",
-    "\n",
-    "    def predict(self, X):\n",
-    "\n",
-    "        \"\"\" Calculates Posterior probability P(c|x) \"\"\"\n",
-    "\n",
-    "        results = []\n",
-    "        X = np.array(X)\n",
-    "\n",
-    "        for query in X:\n",
-    "            probs_outcome = {}\n",
-    "            for outcome in np.unique(self.y_train):\n",
-    "                prior = self.class_priors[outcome]\n",
-    "                likelihood = 1\n",
-    "                evidence = 1\n",
-    "\n",
-    "                for feat, feat_val in zip(self.features, query):\n",
-    "                    likelihood *= self.likelihoods[feat][feat_val + '_' + outcome]\n",
-    "                    evidence *= self.pred_priors[feat][feat_val]\n",
-    "\n",
-    "                posterior = (likelihood * prior) / (evidence)\n",
-    "\n",
-    "                probs_outcome[outcome] = posterior\n",
-    "\n",
-    "            result = max(probs_outcome, key = lambda x: probs_outcome[x])\n",
-    "            results.append(result)\n",
-    "\n",
-    "        return np.array(results)\n",
-    "\n",
-    "\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "\n",
-    "    #Weather Dataset\n",
-    "    print(\"\\nWeather Dataset:\")\n",
-    "\n",
-    "    df = pd.read_table(\"../Data/weather.txt\")\n",
-    "    #print(df)\n",
-    "\n",
-    "    #Split fearures and target\n",
-    "    X,y  = pre_processing(df)\n",
-    "\n",
-    "    nb_clf = NaiveBayes()\n",
-    "    nb_clf.fit(X, y)\n",
-    "\n",
-    "    print(\"Train Accuracy: {}\".format(accuracy_score(y, nb_clf.predict(X))))\n",
-    "\n",
-    "    #Query 1:\n",
-    "    query = np.array([['Rainy','Mild', 'Normal', 't']])\n",
-    "    print(\"Query 1:- {} ---> {}\".format(query, nb_clf.predict(query)))\n",
-    "\n",
-    "    #Query 2:\n",
-    "    query = np.array([['Overcast','Cool', 'Normal', 't']])\n",
-    "    print(\"Query 2:- {} ---> {}\".format(query, nb_clf.predict(query)))\n",
-    "\n",
-    "    #Query 3:\n",
-    "    query = np.array([['Sunny','Hot', 'High', 't']])\n",
-    "    print(\"Query 3:- {} ---> {}\".format(query, nb_clf.predict(query)))"
-   ]
-  }
- ],
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-  "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",
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