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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+IMDB[[:space:]]Dataset.csv filter=lfs diff=lfs merge=lfs -text

IMDB Dataset.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dfc447764f82be365fa9c2beef4e8df89d3919e3da95f5088004797d79695aa2
+size 66212309

app.py

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+import streamlit as st
+import pandas as pd
+import re
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+
+# Streamlit Config
+st.set_page_config(page_title="IMDB Sentiment Analysis", layout="wide")
+st.title("🎬 IMDB Sentiment Analysis")
+
+# Load Dataset
+def load_data():
+    df = pd.read_csv(r"C:\Users\pavilion\Desktop\INNOMATICS_PROJECTS(SL)\IMDB_SENTIMENTAL_ANALYSIS\IMDB Dataset.csv")
+    return df  
+
+df = load_data()
+
+# Display Dataset
+st.write("### 📌 Dataset")
+st.dataframe(df)
+
+# Data Cleaning Function
+def clean_text(text):
+    text = re.sub(r'<.*?>', '', text)  # Remove HTML tags
+    text = re.sub(r'[^a-zA-Z\s]', '', text)  # Keep only letters
+    return text.lower().strip()  
+
+df["review"] = df["review"].apply(clean_text)
+
+st.write("### 🔍 Cleaned Dataset")
+st.dataframe(df)
+
+# Feature Extraction & Model Training
+vectorizer = CountVectorizer(stop_words="english", max_features=10000)  # Limit features
+X = vectorizer.fit_transform(df["review"])  # Keep it sparse
+
+label_encoder = LabelEncoder()
+y = label_encoder.fit_transform(df["sentiment"])
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+
+# Model Accuracy
+accuracy = accuracy_score(y_test, y_pred)
+st.write(f"### 🎯 Model Accuracy: `{accuracy:.2f}`")
+
+# User Input for Prediction
+st.write("### ✍️ Enter Your Movie Review Below")
+text_input = st.text_area("Write your review here...")
+
+if st.button("🔮 Predict Sentiment"):  
+    if text_input.strip():  
+        transformed_text = vectorizer.transform([clean_text(text_input)])  # Keep sparse
+        prediction = model.predict(transformed_text)
+
+        if prediction[0] == 1:
+            st.success("🎉 **Positive Review!**")
+            st.balloons()  
+        else:
+            st.error("❄️ **Negative Review!**")
+            st.snow()  
+    else:
+        st.warning("⚠️ Please enter a review before predicting.")  

requirements.txt

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+Streamlit
+numpy
+pandas
+