$**$PYTHON PLEASE$**$

Objective: To build a machine learning model for sentiment analysis that predicts whether a movie review is positive or negative based on the text content.

Guidelines:

$$ Dataset Selection:

o Download a dataset containing movie reviews labeled as positive or negative sentiments$.$ IMDb or Rotten Tomatoes datasets are good options for this task.

$$ Data Preprocessing:

o Clean the text data by removing punctuation, special characters, and irrelevant symbols.

o Tokenize the text into words or phrases.

o Convert words to lowercase and remove stop words $($common words like 'the', $\text{'}$is$\text{'},$ 'and', etc.$)$ if necessary.

o Encode the positive and negative sentiment labels $($e$.$g$.,0$ for negative, $1$ for positive$).$

$$ Feature Extraction:

o Represent the text data numerically using techniques like Bag$-$of$-$Words $($BoW$),$ TF$-$IDF $($Term Frequency$-$Inverse Document Frequency$),$ or word embeddings $($Word$2$Vec, GloVe$).$

$$ Model Development:

o Use machine learning algorithms like Logistic Regression, Naive Bayes, or a simple neural network $($e$.$g$.,$ a basic feedforward network$)$ to build the sentiment analysis model.

$$ Training and Evaluation:

o Split the dataset into training and testing sets.

o Train the model on the training set and evaluate its performance using the testing set.

o Calculate evaluation metrics such as accuracy, precision, recall, and F$1-$score.

$$ Hyperparameter Tuning $($Optional$)$:

o Experiment with different hyperparameters of the chosen model to improve performance.

Deliverables:

$$ Code Implementation:

o Python code demonstrating data preprocessing, feature extraction, model development, training, and evaluation.

$$ Model Performance Metrics:

o Report the model's accuracy and other relevant metrics on the test set.

$$ Conclusion:

o Summarize the project outcomes, discuss any challenges faced during model development, and suggest potential improvements.

Note: Ensure to properly attribute the source of the movie review dataset and be mindful of any licensing or usage restrictions that might apply.