#machinelearningproject #machinelearningprojectbeginners
GitHub: [ Ссылка ]
💻 Code: [ Ссылка ]
💾 Data: [ Ссылка ]

👇 Topics are covered in this video:
✔️importing the necessary Python libraries and the dataset
✔️prepare the data according to days and hours
✔️look at each day to see on which day the Uber trips were highest
✔️analyse the Uber trips according to the hours
✔️analyse the Uber trips according to the weekdays
✔️look at the correlation of hours and weekdays
✔️plot the density of Uber trips according to the regions of the New Your city


Previous Series:
👉 Mathematics for Data Science ([ Ссылка ])
1) Probability and Statistics for Data Science
2) Solving Optimization Problems
3) Linear Algebra and Learning from Data by Gilbert Strang

👉 Supervised Learning Models ([ Ссылка ])
1) K-Nearest Neighbors
2) Naïve Bayes Classifiers
3) Logistic Regression
4) Linear Regression
5) Support Vector Machine
6) Decision Tree
7) Ensemble Models

👉 Unsupervised Learning Models([ Ссылка ])
1) K Means Clustering
2) Hierarchical Clustering
3) Density-based Clustering (DBSCAN)

👉 Recommendation System & Matrix Factorization([ Ссылка ])
👉Natural Language Processing([ Ссылка ])

👉 Productionization and deployment of ML Models([ Ссылка ])
1) Feature Engineering
2)Productionization and Deployment of ML Models

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