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Dimentinality_reduction_effectsby sreenidhibulusu
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Health-insuranceby ayushiagarwal1408
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BertModel_Classificationby SangamithraPanneerSelvam
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Hand-Controlled-AI-Virtual-Mouseby bharathguntreddi3
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License: No License (No License)This is an AI based Virtual Mouse controlled by our hand gestures and detectors
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Module_7_Assignmentsby debbytech22
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whatsappchatanalysisby chegeveronica
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Fashion_MNIST_Dataset_With_CNNby mertylcndg
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LanguageModelsby SangamithraPanneerSelvam
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Tic-Tac-Toe-Game-by hemasaisrinivas
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Project Glassdoor Analysis
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Calculation-of-business-indicatorsby Gradov270
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License: No License (No License)In the course of the work, an analysis of such business indicators as user conversion, user retention, LTV, ROI and CAC was carried out. Based on these data , a number of conclusions can be drawn: 1. Revenues from users on Android, Mac and iPhone do not differ, unlike low income from PC. However, the cost of advertising on Mac and iPhone devices is much higher than on other devices. Advertising hardly pays off only on a PC device. 2. Users from the USA bring in more revenue on average than users from other countries. At the same time, the cost of advertising aimed at users from the United States is many times higher than in other countries. In addition, such advertising (only in the USA) does not pay off. 3. Users who came thanks to the lambdaMediaAds and TipTop advertising source bring in the most revenue on average. However, the cost of attracting users thanks to the TipTop source is many times higher and advertising does not pay off for TipTop. 4. Users use the application longer if they have registered: a) Devices: PC, b) countries: United Kingdom, France, Germany, c) advertising sources: all except FaceBoom and AdNonSense (for paying users). Recommendations: 1. You can abandon the promotion of the application on Apple platform devices and focus on PC and Android. 2. You can abandon the promotion of the application in the USA and focus on other countries. 3. You can refuse to promote the application by the advertising source TipTop and FaseBoom.
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Work project with Prophet to calculate the forecast of user demand [kW] of the city of Galvez, Santa Fe. Jupyter Notebook.
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IMB-assignment_Jupyterby kshaqinho
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Heart-disease-Prediction-using-ML-by Sonkarp
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Материалы с курса Глеба Михайлова на Степик
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Vehicle-classification-for-drone-camerasby Gshubham101
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Comments-Classificationby jakubgolab
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During the statistical analysis, a dataframe was processed containing the activity of users of the mobile application for food delivery. Data processing was carried out, data verification was carried out, a funnel of events was built and the results of A/A/B testing were analyzed. Results: 1. Duplicates have been deleted, no gaps have been found in the data. 2. The date and time format is given in an acceptable form. 3. The time interval in the analyzed data: from August 1 to August 7 (taking into account the removal of unnecessary data). 4. The funnel of events looks like this: MainScreenAppear OffersScreenAppear CartScreenAppear PaymentScreenSuccessful. 5. The greatest loss of users occurs when switching from the main screen to the list of services 6. The share of users who switch from the main screen event to the payment of the goods is 48 percent. 7. The breakdown of the control groups was carried out correctly, the data were compiled correctly. 8. The number of tests performed when comparing each group is 5. The total number of comparisons is 20. Therefore, the significance level is 0.0025 (according to Bonferoni) 0.0026 (according to Shidak). 9. There is no statistically significant difference between the groups with the original font of the application design and the modified font of the design, therefore, you can stop the experiment and admit that there is no difference in the font of the design. 10. To confirm the conclusion, Bonferroni and Shidak were checked and the result did not change.
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Case_Startup_Fundingby SHIVAM1422000
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License: No License (No License)Data Analysis Using Pandas on Startup Database
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Predicting-Medical-Health-Expensesby Thirumala542
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Deep-Learning-Masteringby sulavojha
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House_price_prediction_using_regressionby M2Shanks
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Twitter_Sentiment_Analysisby SHIVAM1422000
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License: No License (No License)Analysis of the type of tweets.
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Supermarket-Databy prescott00000
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License: No License (No License)From Kaggle Dataset: https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data
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josaa-web-scrapping-dataset-generationby Ankush0077
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License: No License (No License)JoSAA Councelling Dataset Generated Through Web Scrapping
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Repository for data science course.
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Age_Gender_Ethnicity_Prediction_with_Python-mainby fatihilhan42
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License: No License (No License)Through this project, I had the chance to use the tensorflow library for the first time in python. In the content of the project, I developed a project that predicts the age, gender and ethnicity of the people in the pictures with the data we get from the dataset on kaggle. Apart from these, you can see certain tables using data visualization algorithms related to certain data.
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MUSICA tutorial Jupyter notebook files
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GMC-Checkpoint-2by Ghalifisherman
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JPX-Stock-Exchangeby Zheng-Yi-git
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License: No License (No License)capstone of Statistical Learning
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HBO-Max-Recommendation-Systemby aldimeolaalfarisy
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In this project, the analysis of user activity on the site "Articles and not only" was carried out, the topics most interesting to people were identified, the history of activity was shown and a summary table was compiled on the combination of topics from the sources of articles to the topics of articles. All the results were designed in the form of a dashboard.
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Limit-Order-Book-Imbalanceby shubham98r
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stock-sentiment-analysisby ayushiagarwal1408
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accompanying notebooks for yolo tutorial
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my studing
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Assignment_1by RushikeshJaybhaye
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Unsupervised-Learning-Recommenders-Reinforcement-Learningby Ng-Sheng-Tat
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Machine-Learning-Project-Image-Classifierby vineetson
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Dimentinality_reduction_effectsby sreenidhibulusu
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DeepLearningby esmnralican
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Health-insuranceby ayushiagarwal1408
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microarray_imagesby MariosEft97
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BertModel_Classificationby SangamithraPanneerSelvam
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Hand-Controlled-AI-Virtual-Mouseby bharathguntreddi3
This is an AI based Virtual Mouse controlled by our hand gestures and detectors
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Module_7_Assignmentsby debbytech22
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whatsappchatanalysisby chegeveronica
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Fashion_MNIST_Dataset_With_CNNby mertylcndg
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LanguageModelsby SangamithraPanneerSelvam
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Tic-Tac-Toe-Game-by hemasaisrinivas
A Simple Game built using Python
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Glassdoor_Analysisby isa96
Project Glassdoor Analysis
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Calculation-of-business-indicatorsby Gradov270
In the course of the work, an analysis of such business indicators as user conversion, user retention, LTV, ROI and CAC was carried out. Based on these data , a number of conclusions can be drawn: 1. Revenues from users on Android, Mac and iPhone do not differ, unlike low income from PC. However, the cost of advertising on Mac and iPhone devices is much higher than on other devices. Advertising hardly pays off only on a PC device. 2. Users from the USA bring in more revenue on average than users from other countries. At the same time, the cost of advertising aimed at users from the United States is many times higher than in other countries. In addition, such advertising (only in the USA) does not pay off. 3. Users who came thanks to the lambdaMediaAds and TipTop advertising source bring in the most revenue on average. However, the cost of attracting users thanks to the TipTop source is many times higher and advertising does not pay off for TipTop. 4. Users use the application longer if they have registered: a) Devices: PC, b) countries: United Kingdom, France, Germany, c) advertising sources: all except FaceBoom and AdNonSense (for paying users). Recommendations: 1. You can abandon the promotion of the application on Apple platform devices and focus on PC and Android. 2. You can abandon the promotion of the application in the USA and focus on other countries. 3. You can refuse to promote the application by the advertising source TipTop and FaseBoom.
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Prophet_Notebookby Coconaut95
Work project with Prophet to calculate the forecast of user demand [kW] of the city of Galvez, Santa Fe. Jupyter Notebook.
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IMB-assignment_Jupyterby kshaqinho
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Heart-disease-Prediction-using-ML-by Sonkarp
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Real-Estateby OsamaFouad
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A-financial-model-that-groups-previous-loan-applicants-into-three-clustersby clumsybeard
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courseofhlebby probabylity
Материалы с курса Глеба Михайлова на Степик
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Vehicle-classification-for-drone-camerasby Gshubham101
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Comments-Classificationby jakubgolab
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Data-Analysisby AtharvKC
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A_A_B_testingby Gradov270
During the statistical analysis, a dataframe was processed containing the activity of users of the mobile application for food delivery. Data processing was carried out, data verification was carried out, a funnel of events was built and the results of A/A/B testing were analyzed. Results: 1. Duplicates have been deleted, no gaps have been found in the data. 2. The date and time format is given in an acceptable form. 3. The time interval in the analyzed data: from August 1 to August 7 (taking into account the removal of unnecessary data). 4. The funnel of events looks like this: MainScreenAppear OffersScreenAppear CartScreenAppear PaymentScreenSuccessful. 5. The greatest loss of users occurs when switching from the main screen to the list of services 6. The share of users who switch from the main screen event to the payment of the goods is 48 percent. 7. The breakdown of the control groups was carried out correctly, the data were compiled correctly. 8. The number of tests performed when comparing each group is 5. The total number of comparisons is 20. Therefore, the significance level is 0.0025 (according to Bonferoni) 0.0026 (according to Shidak). 9. There is no statistically significant difference between the groups with the original font of the application design and the modified font of the design, therefore, you can stop the experiment and admit that there is no difference in the font of the design. 10. To confirm the conclusion, Bonferroni and Shidak were checked and the result did not change.
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Case_Startup_Fundingby SHIVAM1422000
Data Analysis Using Pandas on Startup Database
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Predicting-Medical-Health-Expensesby Thirumala542
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hands_on_ml_tf_skby HuanDinh20
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Deep-Learning-Masteringby sulavojha
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House_price_prediction_using_regressionby M2Shanks
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Pizza-Exerciseby bejarrodar
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Twitter_Sentiment_Analysisby SHIVAM1422000
Analysis of the type of tweets.
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Supermarket-Databy prescott00000
From Kaggle Dataset: https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data
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josaa-web-scrapping-dataset-generationby Ankush0077
JoSAA Councelling Dataset Generated Through Web Scrapping
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TIC-TAC-TOC-botby Amo-494
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lca3-dclmby LuanaCamposA
Repository for data science course.
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Car_Evaluation_using_KNNby M2Shanks
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TESTby lana7am
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Age_Gender_Ethnicity_Prediction_with_Python-mainby fatihilhan42
Through this project, I had the chance to use the tensorflow library for the first time in python. In the content of the project, I developed a project that predicts the age, gender and ethnicity of the people in the pictures with the data we get from the dataset on kaggle. Apart from these, you can see certain tables using data visualization algorithms related to certain data.
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musica-tutorialby cemacrr
MUSICA tutorial Jupyter notebook files
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GMC-Checkpoint-2by Ghalifisherman
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JPX-Stock-Exchangeby Zheng-Yi-git
capstone of Statistical Learning
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HBO-Max-Recommendation-Systemby aldimeolaalfarisy
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Dashboard_analysisby Gradov270
In this project, the analysis of user activity on the site "Articles and not only" was carried out, the topics most interesting to people were identified, the history of activity was shown and a summary table was compiled on the combination of topics from the sources of articles to the topics of articles. All the results were designed in the form of a dashboard.
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Limit-Order-Book-Imbalanceby shubham98r
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stock-sentiment-analysisby ayushiagarwal1408
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YOLO7by GabrielleDompreh
accompanying notebooks for yolo tutorial
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tgu_data_analystby qwerty8909
my studing
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Einesby Sassenach21
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Assignment_1by RushikeshJaybhaye
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Unsupervised-Learning-Recommenders-Reinforcement-Learningby Ng-Sheng-Tat
Machine Learning Specialization Course 3
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Machine-Learning-Project-Image-Classifierby vineetson
Machine learning project for classification of images.
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