diff --git a/Project_Outline.ipynb b/Project_Outline.ipynb
index e47f144..bfb56e8 100644
--- a/Project_Outline.ipynb
+++ b/Project_Outline.ipynb
@@ -1 +1,97 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Project Outline.ipynb","provenance":[],"authorship_tag":"ABX9TyPZl4d0nA5Qmq8X1mDqSb1O"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Title of Project**"],"metadata":{"id":"dqZ-nhxiganh"}},{"cell_type":"markdown","source":["-------------"],"metadata":{"id":"gScHkw6jjrLo"}},{"cell_type":"markdown","source":["## **Objective**"],"metadata":{"id":"Xns_rCdhh-vZ"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"9sPvnFM1iI9l"}},{"cell_type":"markdown","source":["## **Data Source**"],"metadata":{"id":"-Vbnt9CciKJP"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"sGcv5WqQiNyl"}},{"cell_type":"markdown","source":["## **Import Library**"],"metadata":{"id":"r7GrZzX0iTlV"}},{"cell_type":"code","source":[""],"metadata":{"id":"UkK6NH9DiW-X"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Import Data**"],"metadata":{"id":"9lHPQj1XiOUc"}},{"cell_type":"code","source":[""],"metadata":{"id":"zcU1fdnGho6M"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Describe Data**"],"metadata":{"id":"7PUnimBoiX-x"}},{"cell_type":"code","source":[""],"metadata":{"id":"kG15arusiZ8Z"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Visualization**"],"metadata":{"id":"oBGX4Ekniriz"}},{"cell_type":"code","source":[""],"metadata":{"id":"lW-OIRK0iuzO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Preprocessing**"],"metadata":{"id":"UqfyPOCYiiww"}},{"cell_type":"code","source":[""],"metadata":{"id":"3cyr3fbGin0A"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Define Target Variable (y) and Feature Variables (X)**"],"metadata":{"id":"2jXJpdAuiwYW"}},{"cell_type":"code","source":[""],"metadata":{"id":"QBCakTuli57t"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Train Test Split**"],"metadata":{"id":"90_0q_Pbi658"}},{"cell_type":"code","source":[""],"metadata":{"id":"u60YYaOFi-Dw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Modeling**"],"metadata":{"id":"cIhyseNria7W"}},{"cell_type":"code","source":[""],"metadata":{"id":"Toq58wpkjCw7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Model Evaluation**"],"metadata":{"id":"vhAwWfG0jFun"}},{"cell_type":"code","source":[""],"metadata":{"id":"lND3jJj_jhx4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Prediction**"],"metadata":{"id":"8AzwG7oLjiQI"}},{"cell_type":"code","source":[""],"metadata":{"id":"JLebGzDJjknA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Explaination**"],"metadata":{"id":"SBo38CJZjlEX"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"Ybi8FR9Kjv00"}}]}
\ No newline at end of file
+*Project Title:* "StyleSentiment"
+
+*Objective:*
+
+Develop a Multinomial Naive Bayes (MNB) model to predict the sentiment (Positive/Negative/Neutral) of women's clothing reviews based on text analysis.
+
+*Dataset:*
+
+- Collect 5,000+ reviews from online fashion retailers (e.g., Amazon, Zappos, ASOS)
+- Filter reviews for women's clothing only
+- Preprocess data:
+    1. Tokenization
+    2. Stopword removal
+    3. Stemming/Lemmatization
+    4. Remove special characters and punctuation
+    5. Label encoding (Positive: 1, Negative: 0, Neutral: 2)
+
+*Features:*
+
+1. Text data (review content)
+2. Rating (1-5 stars)
+
+*Target Variable:*
+
+Sentiment (Positive/Negative/Neutral)
+
+*Multinomial Naive Bayes (MNB) Model:*
+
+1. Import necessary libraries: `numpy`, `pandas`, `scikit-learn`
+2. Split data into training (80%) and testing sets (20%)
+3. Create MNB model: `MultinomialNB()`
+4. Train model on training data
+5. Evaluate model on testing data
+
+*Evaluation Metrics:*
+
+1. Accuracy
+2. Precision
+3. Recall
+4. F1-score
+5. ROC-AUC
+
+*Code:*
+```
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Load data
+df = pd.read_csv('reviews.csv')
+
+# Preprocess data
+vectorizer = CountVectorizer(stop_words='english')
+X = vectorizer.fit_transform(df['review'])
+y = df['sentiment']
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train MNB model
+mnb = MultinomialNB()
+mnb.fit(X_train, y_train)
+
+# Evaluate model
+y_pred = mnb.predict(X_test)
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Classification Report:")
+print(classification_report(y_test, y_pred))
+print("Confusion Matrix:")
+print(confusion_matrix(y_test, y_pred))
+```
+
+*Expected Results:*
+
+- Accuracy: 85-90%
+- Precision: 80-85%
+- Recall: 85-90%
+- F1-score: 85-90%
+- ROC-AUC: 0.9-0.95
+
+*Timeline:*
+
+- Data collection and preprocessing: 2 days
+- Model development and evaluation: 3 days
+- Testing and refinement: 1 day
+
+Total estimated time: 6 days
+
+Would you like to:
+
+1. Discuss dataset collection strategies
+2. Explore hyperparameter tuning for MNB
+3. Compare MNB with other machine learning models
+4. Implement additional features (e.g., product category, rating)
+5. Something else (please specify)