�ݺ�ߣshows by User: LucindaLinde

�ݺ�ߣshows by User: LucindaLinde / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: LucindaLinde / Sun, 29 Mar 2020 00:47:58 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: LucindaLinde Wooing the Best Bank Deposit Customers /slideshow/wooing-the-best-bank-deposit-customers/231045523 eai6000finalpresentationlucindalinde-200329004758
Applying machine learning to Kaggle data set to predict which customers are most likely to become customers. Random Forest column importance graph is helpful to prioritize the best segments to target.]]>
Applying machine learning to Kaggle data set to predict which customers are most likely to become customers. Random Forest column importance graph is helpful to prioritize the best segments to target.]]> Sun, 29 Mar 2020 00:47:58 GMT /slideshow/wooing-the-best-bank-deposit-customers/231045523 LucindaLinde@slideshare.net(LucindaLinde) Wooing the Best Bank Deposit Customers LucindaLinde Applying machine learning to Kaggle data set to predict which customers are most likely to become customers. Random Forest column importance graph is helpful to prioritize the best segments to target. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eai6000finalpresentationlucindalinde-200329004758-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Applying machine learning to Kaggle data set to predict which customers are most likely to become customers. Random Forest column importance graph is helpful to prioritize the best segments to target.

Wooing the Best Bank Deposit Customers from Lucinda Linde

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0 Predicting Tweet Sentiment /slideshow/predicting-tweet-sentiment/157531417 predictingtweetsentiment-190724135110
Objective of the Project Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling. ]]>
Objective of the Project Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling. ]]> Wed, 24 Jul 2019 13:51:10 GMT /slideshow/predicting-tweet-sentiment/157531417 LucindaLinde@slideshare.net(LucindaLinde) Predicting Tweet Sentiment LucindaLinde Objective of the Project Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/predictingtweetsentiment-190724135110-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Objective of the Project Tweet sentiment analysis gives businesses insights into customers and competitors. In this project, we combined several text preprocessing techniques with machine learning algorithms. Neural network, Random Forest and Logistic Regression models were trained on the Sentiment140 twitter data set. We then predicted the sentiment of a hold-out test set of tweets. We used both Python and PySpark (local Spark Context) to program different parts of the pre-processing and modelling.

Predicting Tweet Sentiment from Lucinda Linde

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0 https://cdn.slidesharecdn.com/profile-photo-LucindaLinde-48x48.jpg?cb=1586961725 Lucinda Linde I invest in and consult to early stage information technology companies. My investments at First Light Capital included Nellymoser, Incipient, KESI, Strong Numbers (now part of Intuit's "It's Deductible" product) and HubX (acquired by SynXis). My personal angel investments included Visualization Technologies Inc. (acquired by GE Medical Systems), Viveca (acquired by Open Pages) and Collego (acquired by MRO Systems) and Softrax (profitable company). I co-authored the VSS Project: Report on Angel Investors, for Ken Morse of the MIT Entrepreneurship Center and Professor Howard Stevenson at HBS. Previously, I helped build a management consulting firm focused on the telecommu... https://cdn.slidesharecdn.com/ss_thumbnails/eai6000finalpresentationlucindalinde-200329004758-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/wooing-the-best-bank-deposit-customers/231045523 Wooing the Best Bank D... https://cdn.slidesharecdn.com/ss_thumbnails/predictingtweetsentiment-190724135110-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/predicting-tweet-sentiment/157531417 Predicting Tweet Senti...