In this project movie recommendation system, various tools and techniques are utilized to recommend movies based on multiple parameters such as genres, keywords, cast, director, and more. The dataset is first preprocessed using pandas, with missing values filled appropriately. TF-IDF (Term Frequency-Inverse Document Frequency) from the sklearn library is used to convert text data (movie attributes) into feature vectors, capturing the importance of words within the dataset.
Cosine similarity is then applied to measure the similarity between the feature vectors, which helps identify movies that are most similar to the one inputted by the user. Difflib is used to find close matches for the user-provided movie title to ensure accuracy in searching. Finally, the most similar movies are displayed by sorting the similarity scores and presenting the top recommendations.

The diabetes prediction project begins with importing necessary libraries such as pandas, numpy, and machine learning tools like LogisticRegression and SVC. The dataset is read from a CSV file, and exploratory data analysis (EDA) reveals no missing values, no object types, and some false values in the columns. Age is converted into a categorical variable to better interpret the results.
Next, the dataset is examined for correlations between features, which are found to be weak. EDA is visualized using bar plots for features such as Pregnancies, Glucose, and BMI, where some patterns like higher pregnancies and glucose levels are observed in diabetic individuals. Outliers in the dataset are handled by capping extreme values for features such as pregnancies, glucose, blood pressure, and insulin.
After preprocessing, a logistic regression model is trained using the processed features. The model achieves an accuracy of around 75%, and hyperparameter tuning is done using GridSearchCV to improve performance. The optimized logistic regression model slightly improves the accuracy. Additionally, support vector machine (SVM) and decision tree models are built and optimized using RandomizedSearchCV, with each achieving competitive performance metrics.
Finally, hyperparameter tuning for SVM and decision tree models further refines the performance of the models, and confusion matrices, classification reports, and ROC-AUC scores are calculated to evaluate the predictive accuracy of the models.

This Audi car price prediction project employs multiple machine learning regression techniques to predict the prices of Audi cars based on various features such as model, year, mileage, fuel type, and engine size. The dataset undergoes preprocessing, including label encoding for categorical features like transmission and fuel type, allowing the models to interpret the non-numeric data.
The project begins by splitting the data into training and testing sets and evaluating different regression models. First, Linear Regression is applied to establish a basic baseline for price prediction. This is followed by the Support Vector Regressor (SVR), which is particularly useful for capturing non-linear relationships within the data. To improve model performance, ensemble methods like Random Forest Regressor and Extra Tree Regressor are also tested. These models work by combining multiple decision trees to enhance prediction accuracy and handle data variance better.
To fine-tune the Random Forest model, both RandomizedSearchCV and GridSearchCV are used for hyperparameter tuning. RandomizedSearchCV provides faster results by sampling a set of hyperparameters, while GridSearchCV exhaustively tests all combinations for the best result, though at the cost of longer computation time. The CatBoost Regressor is also explored as a more specialized method for handling categorical data without the need for extensive preprocessing.
For performance evaluation, metrics such as R-squared, Mean Squared Error (MSE), and Mean Absolute Error (MAE) are used to compare the models. The project concludes by serializing the final model using Pickle, making it deployable for future predictions without needing to retrain it from scratch.
This comprehensive approach demonstrates how different machine learning techniques can be applied to regression problems, with each method's strengths and limitations highlighted throughout the process.

This project for movie genre classification employs a combination of Natural Language Processing (NLP) and machine learning techniques to predict a film's genre based on its plot summary. The process begins with data loading using the pandas library, which is responsible for handling the datasets containing movie descriptions and genres. The datasets are read from text files and structured into DataFrames for easier manipulation and analysis.
Text preprocessing is a critical step in preparing the data for machine learning. The nltk library is utilized for natural language processing tasks, such as tokenization and stopword removal. A custom function is defined to clean the plot summaries by converting text to lowercase, removing URLs and punctuation, and eliminating common English stopwords, thereby retaining only the meaningful words that contribute to the classification task. The cleaned text is stored in a new column within the DataFrame.
To convert the cleaned text into a numerical format that machine learning algorithms can interpret, the code employs the TfidfVectorizer from sklearn This technique transforms the text data into TF-IDF vectors, capturing the importance of words in relation to the overall corpus. These vectors serve as input features for the classification models.
The models used for classification include Multinomial Naive Bayes, Logistic Regression, and Support Vector Classifier (SVC), all from the sklearn library. The data is split into training and testing sets using train_test_split, allowing for model training and evaluation. Each model is trained on the training data and subsequently evaluated on the test data to assess its accuracy and performance, using metrics such as accuracy score and classification report.
To visualize the performance of each classifier, a pie chart is created, showing the accuracy of each model in predicting the genres. The results are compiled into a new DataFrame that includes the predicted genres alongside the actual genres from the test solution dataset, enabling a direct comparison. Finally, the trained TF-IDF vectorizer and the Logistic Regression model are saved to disk using the pickle library for future predictions on new movie descriptions.
Overall, this code provides a comprehensive pipeline for movie genre classification, showcasing the effective use of text preprocessing, feature extraction, and multiple machine learning models to achieve accurate genre predictions based on textual data.

In this project, credit card fraud detection is achieved through a systematic application of data preprocessing, exploratory data analysis, and machine learning techniques. The dataset consists of two parts: a training dataset with 1,296,675 rows and a test dataset with 555,719 rows, both containing 23 columns. The target variable, `is_fraud`, indicates whether a transaction is fraudulent (1) or genuine (0).

Data Import and Initial Processing

The project begins by importing essential libraries such as pandas, numpy, matplotlib, and seaborn for data manipulation and visualization. It also includes various machine learning libraries from scikit-learn, such as LogisticRegression, KNeighborsClassifier, RandomForestClassifier, and SVC for building and evaluating models. The datasets are read into DataFrames, and unnecessary columns are dropped to streamline the analysis.

Data Preprocessing

The project performs critical data preprocessing steps, including converting date columns from object types to datetime types. It generates new features based on existing data, such as categorizing transactions by the time of day (morning, afternoon, evening, night) and age groups (Young, Middle_age, Old) based on the date of birth. This enhances the dataset's richness and provides better context for model training.

The project further employs one-hot encoding to convert categorical variables into numerical formats suitable for machine learning algorithms, ensuring the model can interpret them effectively. The dataset is split into training and testing sets using train_test_split, with 70% of the data reserved for training and 30% for testing.

Model Training and Evaluation

Multiple machine learning models are applied to the training data, including Logistic Regression, K-Neighbors Classifier, Random Forest Classifier, and Support Vector Classifier (SVC). Each model's performance is assessed using metrics such as accuracy, precision, recall, and F1-score, which provide insights into their effectiveness in classifying fraudulent transactions. Notably, the K-Neighbors Classifier outperforms other models with an accuracy of 99.5%, followed closely by Logistic Regression at >99.4%.

Hyperparameter tuning is implemented for the Random Forest Classifier using Grid Search to optimize model performance further, although it takes considerably longer to train. The SVC model is also explored, with the data standardized using StandardScaler to improve its training efficiency.

Conclusion and Recommendations

In conclusion, the project demonstrates a comprehensive approach to credit card fraud detection by utilizing a variety of data processing techniques and machine learning models. The high accuracy achieved indicates the models robustness, making them suitable for practical application in fraud detection systems. For future work, exploring deployment strategies while maintaining one-hot encoding or utilizing label encoding can facilitate integration into web applications, enabling real-time fraud detection capabilities.

The project implements a spam detection system using various libraries and techniques to process and analyze textual data. Initially, it employs Pandas to load and manipulate the dataset, which consists of SMS messages labeled as either spam or ham. The data undergoes preprocessing steps, including removing duplicates, renaming columns, and calculating features such as the number of characters, words, and sentences in each message, which aids in understanding the text's structure.
Text normalization techniques are applied, where all text is converted to lowercase, and unwanted characters are removed using regex. Additionally, the code utilizes NLTK to tokenize the text, eliminate stop words, and apply stemming, which reduces words to their root forms. This transformation results in a cleaner and more meaningful dataset. Following this, CountVectorizer is used to convert the processed text into a numerical format suitable for machine learning models.
For the classification task, the dataset is divided into training and testing sets using train_test_split. The models employed include Gaussian Naive Bayes and Bernoulli Naive Bayes, which are trained on the training dataset and then evaluated on the test set. The code concludes by printing the training and testing accuracy scores for both models, providing insight into their effectiveness in distinguishing between spam and ham messages. Overall, this code represents a comprehensive pipeline for building a spam detection system using natural language processing and machine learning techniques.

This project represents your first analysis project, focusing on the well-known Iris dataset, which is commonly used for classification tasks in machine learning. The project employs several libraries, including NumPy, Pandas, Matplotlib and Seaborn, to facilitate data manipulation, visualization, and analysis. The initial steps involve loading the dataset using Seaborn and exploring its structure through various methods, such as head(), isnull(), and describe(), which provide insights into the data distribution, missing values, and overall information about the dataset.
After the exploratory data analysis (EDA), the features and target variables are defined. The feature set (X) consists of the four measurements (sepal length, sepal width, petal length, and petal width), while the target variable (y) contains the species of the iris plants. A correlation matrix is generated to assess the relationships between the features, visualized using a heatmap, allowing for an understanding of how the features interact with each other.
For the modeling phase, the dataset is split into training and testing subsets using train_test_split from Scikit-learn. A Logistic Regression classifier is chosen for this classification task. To optimize the model's hyperparameters, GridSearchCV is employed, which systematically explores various combinations of parameters such as penalty types, regularization strength (C), and maximum iterations. The best parameters and the corresponding accuracy score are identified upon fitting the model.
Once trained, predictions are made on the test dataset, and the performance is evaluated using accuracy scores and a classification report that provides detailed metrics for each class. Finally, the code includes an example of making predictions for specific flower measurements, showcasing the model's practical application. This project not only demonstrates fundamental data analysis and modeling techniques but also serves as a foundational experience in applying machine learning to real-world data.

In this project, I conduct an analysis of the Titanic dataset to predict passenger survival outcomes using Logistic Regression. The project begins by importing essential libraries such as Pandas, NumPy, Matplotlib, and Seaborn for data manipulation, visualization, and analysis. The dataset is loaded from a CSV file, and initial data exploration is performed using methods like `head()`, `nunique()`, and `describe()` to understand the data structure, unique values, and statistical summaries.
The analysis continues with an investigation of missing values, specifically in the 'Age' column, and an examination of survival counts across different passenger classes using **Seaborn's** `countplot`. The categorical variable 'Sex' is encoded into numerical values using **LabelEncoder** to facilitate model training. This is followed by visualizing the survival distribution based on sex, which reveals insights into how gender influences survival rates.
To handle the missing values in the 'Age' column, the median age is computed and used to fill any gaps, ensuring a complete dataset for modeling. The features selected for prediction are 'Pclass', 'Sex', and 'Age', while the target variable is 'Survived'. The dataset is then split into training and testing sets using **train_test_split** from **Scikit-learn**.
The Logistic Regression classifier is trained on the training set, and predictions are made on the test set. The model's performance is evaluated using accuracy scores and a classification report, which provides detailed metrics for understanding the model's effectiveness. Finally, the code includes examples of predicting the survival outcome for specific passengers based on their class, gender, and age, demonstrating the practical application of the model. This project not only showcases your ability to apply machine learning techniques but also highlights your skills in data preprocessing, visualization, and model evaluation in a real-world context.