pankajbarai / projects

Data Science Projects

Welcome to My Project....

1) Exploratory_Data_Analysis_on_Netflix_Movies_and_TV_Shows.

Dataset is available on Kaggle link.
It contains information about the NetFlix movies and TV shows.It includes the information about type of the movie,title of the movie, director name, cast, country,date_added, release_year,rating of the movie, duration, listed_in and descriptionthe.
We will analyze this dataset and draw some conclusions.

Libraries used: Pandas, Numpy , Matplotlib, Seaborn , Ploty, Wordcloud....
Language : Python.
Environment : Google Colab link.

2) Predict Employee Attrition using Classification Algorithms.

Dataset link.
Libraries used :
Pandas,Numpy,Matplotlib,Seaborn,Plotly,Sidetable,Pandas_Profiling,LabelEncoder, StandardScaler train_test_split , GridSearchCV,pycaret.
ML Algorithm used : LogisticRegression, SVC, KNeighborsClassifier, GaussianNB, DecisionTreeClassifier, RandomForestClassifier.
Deployed : Streamlit. Language : Python.
Environment : Google Colab link.

3) Unsupervised Learning (Customer Market Segmentation).

Problem Statement:
You have been hired as a Data Scientist and have to perform Customer Market Segementation which will help the bank's marketing team to launch a targetted marketing ad campaign that is tailored to a specific group of customers.
Aim:
you have to build a clustering model to divide customer's into distinctive groups.
Dataset link.
Libraries used:
Pandas,Numpy,Matplotlib,Seaborn,Plotly,Sidetable,Pandas_Profiling,pycaret
ML Algorithm used : Clustering, DBSCAN, PCA.
Language : Python.
Environment : Google Colab [link](https://github.com/PankajBarai/Projects/tree/main/Unsupervised%20Learning%20(Customer%20Market%20Segmentation).

4) Facial Emotion Recognition using CNN.

Libraries used:
Pandas,Numpy,Matplotlib,Seaborn,Tensorflow,Keras,ImageDataGenerator.
Language : Python.
Environment : Google Colab link.

5)House Price Prediction with Linear Regression.

Libraries used:
Pandas,Numpy,Matplotlib,Seaborn.
ML Algorithm used : Linear Regression.
Language : Python.
Environment : Google Colab link.

6)Cab Booking System.

Libraries used:
Pandas,Numpy,Matplotlib,Seaborn, Pandas-profiling.
ML Algorithm used : LogisticRegression,RandomForestRegressor,KNeighborsRegressor,GridSearchCV.
Language : Python.
Environment : Google Colab link.

6)Rain in Australia.

Dataset is available on Kaggle link
Libraries used:
Pandas,Numpy,Matplotlib,Seaborn, Pandas-profiling.
ML Algorithm used : EDA, Simpleimputer, MinMaxScaler,Label Encoder, RandomForest,RandomSearch, Pickel
Language : Python.
Environment : Google Colab link.