naiye bayes.ipynb

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     "text": [
      "Accuracy: 66.67%\n"
     ]
    }
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   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "data = {\n",
    "    'Outlook': ['Sunny', 'Sunny', 'Overcast', 'Rain', 'Rain', 'Rain', 'Overcast', 'Sunny', 'Sunny', 'Rain', 'Sunny', 'Overcast', 'Overcast', 'Rain'],\n",
    "    'Temperature': ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool', 'Mild', 'Cool', 'Mild', 'Mild', 'Mild', 'Hot', 'Mild'],\n",
    "    'Humidity': ['High', 'High', 'High', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'High'],\n",
    "    'Wind': ['Weak', 'Strong', 'Weak', 'Weak', 'Weak', 'Strong', 'Strong', 'Weak', 'Weak', 'Weak', 'Strong', 'Strong', 'Weak', 'Strong'],\n",
    "    'PlayTennis': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No']\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df = pd.get_dummies(df, columns=['Outlook', 'Temperature', 'Humidity', 'Wind'])\n",
    "X = df.drop('PlayTennis', axis=1)\n",
    "y = df['PlayTennis']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy * 100:.2f}%')"
   ]
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     "text": [
      "Accuracy: 0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "nb_classifier = GaussianNB()\n",
    "nb_classifier.fit(X_train, y_train)\n",
    "y_pred = nb_classifier.predict(X_test)\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n"
   ]
  },
  {
   "cell_type": "code",
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     "name": "stderr",
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     "text": [
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     C:\\Users\\hp\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\movie_reviews.zip.\n"
     ]
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    {
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     "text": [
      "Accuracy: 73.50%\n",
      "The review is classified as: positive\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import nltk\n",
    "from nltk.corpus import movie_reviews\n",
    "from nltk.classify import NaiveBayesClassifier\n",
    "from nltk.classify.util import accuracy\n",
    "\n",
    "# Download the movie_reviews dataset\n",
    "nltk.download('movie_reviews')\n",
    "\n",
    "# Function to extract features from the movie reviews\n",
    "def extract_features(words):\n",
    "    return dict([(word, True) for word in words])\n",
    "\n",
    "# Load positive and negative reviews from the movie_reviews dataset\n",
    "positive_reviews = [(extract_features(movie_reviews.words(fileids=[f])), 'positive') for f in movie_reviews.fileids('pos')]\n",
    "negative_reviews = [(extract_features(movie_reviews.words(fileids=[f])), 'negative') for f in movie_reviews.fileids('neg')]\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "split_ratio = 0.8\n",
    "positive_split = int(len(positive_reviews) * split_ratio)\n",
    "negative_split = int(len(negative_reviews) * split_ratio)\n",
    "\n",
    "train_set = positive_reviews[:positive_split] + negative_reviews[:negative_split]\n",
    "test_set = positive_reviews[positive_split:] + negative_reviews[negative_split:]\n",
    "\n",
    "# Train the Naive Bayes classifier\n",
    "classifier = NaiveBayesClassifier.train(train_set)\n",
    "\n",
    "# Test the accuracy of the classifier\n",
    "accuracy_percentage = accuracy(classifier, test_set)\n",
    "print(f'Accuracy: {accuracy_percentage * 100:.2f}%')\n",
    "\n",
    "# Example of classifying a new review\n",
    "new_review = \"This movie was excellent! I loved every moment of it.\"\n",
    "new_review_features = extract_features(new_review.split())\n",
    "classification = classifier.classify(new_review_features)\n",
    "print(f'The review is classified as: {classification}')"
   ]
  }
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