end to end predictive model using python

15.2.2023

When we inform you of an increase in Uber fees, we also inform drivers. To determine the ROC curve, first define the metrics: Then, calculate the true positive and false positive rates: Next, calculate the AUC to see the model's performance: The AUC is 0.94, meaning that the model did a great job: If you made it this far, well done! The idea of enabling a machine to learn strikes me. NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) in Python using Pytorch. If you are interested to use the package version read the article below. Consider this exercise in predictive programming in Python as your first big step on the machine learning ladder. There are various methods to validate your model performance, I would suggest you to divide your train data set into Train and validate (ideally 70:30) and build model based on 70% of train data set. Both linear regression (LR) and Random Forest Regression (RFR) models are based on supervised learning and can be used for classification and regression. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. The target variable (Yes/No) is converted to (1/0) using the codebelow. Popular choices include regressions, neural networks, decision trees, K-means clustering, Nave Bayes, and others. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. End to End Predictive model using Python framework. Please read my article below on variable selection process which is used in this framework. The variables are selected based on a voting system. This is easily explained by the outbreak of COVID. A couple of these stats are available in this framework. pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED']), from bokeh.io import push_notebook, show, output_notebook, output_notebook()from sklearn import metrics, preds = clf.predict_proba(features_train)[:,1]fpr, tpr, _ = metrics.roc_curve(np.array(label_train), preds), auc = metrics.auc(fpr,tpr)p = figure(title="ROC Curve - Train data"), r = p.line(fpr,tpr,color='#0077bc',legend = 'AUC = '+ str(round(auc,3)), line_width=2), s = p.line([0,1],[0,1], color= '#d15555',line_dash='dotdash',line_width=2), 3. 7 Dropoff Time 554 non-null object Barriers to workflow represent the many repetitions of the feedback collection required to create a solution and complete a project. Next, we look at the variable descriptions and the contents of the dataset using df.info() and df.head() respectively. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); How to Read and Write With CSV Files in Python.. I did it just for because I think all the rides were completed on the same day (believe me, Im looking forward to that ! b. existing IFRS9 model and redeveloping the model (PD) and drive business decision making. f. Which days of the week have the highest fare? Support for a data set with more than 10,000 columns. Please read my article below on variable selection process which is used in this framework. The Random forest code is providedbelow. Data security and compliance features. The corr() function displays the correlation between different variables in our dataset: The closer to 1, the stronger the correlation between these variables. Predictive analysis is a field of Data Science, which involves making predictions of future events. Companies from all around the world are utilizing Python to gather bits of knowledge from their data. You will also like to specify and cache the historical data to avoid repeated downloading. Machine learning model and algorithms. Our objective is to identify customers who will churn based on these attributes. In Michelangelo, users can submit models through our web UI for convenience or through our integration API with external automation tools. 39.51 + 15.99 P&P . This could be an alarming indicator, given the negative impact on businesses after the Covid outbreak. d. What type of product is most often selected? It is mandatory to procure user consent prior to running these cookies on your website. Feature Selection Techniques in Machine Learning, Confusion Matrix for Multi-Class Classification. Identify data types and eliminate date and timestamp variables, We apply all the validation metric functions once we fit the data with all these algorithms, https://www.kaggle.com/shrutimechlearn/churn-modelling#Churn_Modelling.cs. First, we check the missing values in each column in the dataset by using the below code. 10 Distance (miles) 554 non-null float64 Some basic formats of data visualization and some practical implementation of python libraries for data visualization. Most of the masters on Kaggle and the best scientists on our hackathons have these codes ready and fire their first submission before making a detailed analysis. I focus on 360 degree customer analytics models and machine learning workflow automation. Some of the popular ones include pandas, NymPy, matplotlib, seaborn, and scikit-learn. This guide is the first part in the two-part series, one with Preprocessing and Exploration of Data and the other with the actual Modelling. Here is the link to the code. This category only includes cookies that ensures basic functionalities and security features of the website. For starters, if your dataset has not been preprocessed, you need to clean your data up before you begin. RangeIndex: 554 entries, 0 to 553 Here is the link to the code. If we do not think about 2016 and 2021 (not full years), we can clearly see that from 2017 to 2019 mid-year passengers are 124, and that there is a significant decrease from 2019 to 2020 (-51%). Rarely would you need the entire dataset during training. Lets go through the process step by step (with estimates of time spent in each step): In my initial days as data scientist, data exploration used to take a lot of time for me. Today we are going to learn a fascinating topic which is How to create a predictive model in python. <br><br>Key Technical Activities :<br> I have delivered 5+ end to end TM1 projects covering wider areas of implementation such as:<br> Integration . This will take maximum amount of time (~4-5 minutes). Step 1: Understand Business Objective. Analytics Vidhya App for the Latest blog/Article, (Senior) Big Data Engineer Bangalore (4-8 years of Experience), Running scalable Data Science on Cloud with R & Python, Build a Predictive Model in 10 Minutes (using Python), We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. And the number highlighted in yellow is the KS-statistic value. Now, we have our dataset in a pandas dataframe. Each model in scikit-learn is implemented as a separate class and the first step is to identify the class we want to create an instance of. From the ROC curve, we can calculate the area under the curve (AUC) whose value ranges from 0 to 1. You can look at 7 Steps of data exploration to look at the most common operations ofdata exploration. In addition, the hyperparameters of the models can be tuned to improve the performance as well. And we call the macro using the code below. We use different algorithms to select features and then finally each algorithm votes for their selected feature. In order to predict, we first have to find a function (model) that best describes the dependency between the variables in our dataset. However, we are not done yet. For Example: In Titanic survival challenge, you can impute missing values of Age using salutation of passengers name Like Mr., Miss.,Mrs.,Master and others and this has shown good impact on model performance. Uber rides made some changes to gain the trust of their customer back after having a tough time in covid, changing the capacity, safety precautions, plastic sheets between driver and passenger, temperature check, etc. These cookies will be stored in your browser only with your consent. The baseline model IDF file containing all the design variables and components of the building energy model is imported into the Python program. Before getting deep into it, We need to understand what is predictive analysis. NumPy sign()- Returns an element-wise indication of the sign of a number. These include: Strong prices help us to ensure that there are always enough drivers to handle all our travel requests, so you can ride faster and easier whether you and your friends are taking this trip or staying up to you. You also have the option to opt-out of these cookies. Thats because of our dynamic pricing algorithm, which converts prices according to several variables, such as the time and distance of your route, traffic, and the current need of the driver. We can use several ways in Python to build an end-to-end application for your model. In this article, we discussed Data Visualization. This type of pipeline is a basic predictive technique that can be used as a foundation for more complex models. Depending upon the organization strategy, business needs different model metrics are evaluated in the process. Heres a quick and easy guide to how Ubers dynamic price model works, so you know why Uber prices are changing and what regular peak hours are the costs of Ubers rise. jan. 2020 - aug. 20211 jaar 8 maanden. Analyzing the compared data within a range that is o to 1 where 0 refers to 0% and 1 refers to 100 %. Append both. Hey, I am Sharvari Raut. Applications include but are not limited to: As the industry develops, so do the applications of these models. The basic cost of these yellow cables is $ 2.5, with an additional $ 0.5 for each mile traveled. With the help of predictive analytics, we can connect data to . The following tabbed examples show how to train and. We can create predictions about new data for fire or in upcoming days and make the machine supportable for the same. We have scored our new data. Predictive modeling is always a fun task. Since not many people travel through Pool, Black they should increase the UberX rides to gain profit. I have seen data scientist are using these two methods often as their first model and in some cases it acts as a final model also. We found that the same workflow applies to many different situations, including traditional ML and in-depth learning; surveillance, unsupervised, and under surveillance; online learning; batches, online, and mobile distribution; and time-series predictions. 9 Dropoff Lng 525 non-null float64 Working closely with Risk Management team of a leading Dutch multinational bank to manage. End to End Project with Python | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster Also, Michelangelos feature shop is important in enabling teams to reuse key predictive features that have already been identified and developed by other teams. However, we are not done yet. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. Finally, you evaluate the performance of your model by running a classification report and calculating its ROC curve. I recommend to use any one ofGBM/Random Forest techniques, depending on the business problem. 2.4 BRL / km and 21.4 minutes per trip. Python Python is a general-purpose programming language that is becoming ever more popular for analyzing data. In general, the simplest way to obtain a mathematical model is to estimate its parameters by fixing its structure, referred to as parameter-estimation-based predictive control . So, if you want to know how to protect your messages with end-to-end encryption using Python, this article is for you. Precision is the ratio of true positives to the sum of both true and false positives. Here, clf is the model classifier object and d is the label encoder object used to transform character to numeric variables. These cookies do not store any personal information. The major time spent is to understand what the business needs and then frame your problem. Considering the whole trip, the average amount spent on the trip is 19.2 BRL, subtracting approx. Applied Data Science Using PySpark Learn the End-to-End Predictive Model-Building Cycle Ramcharan Kakarla Sundar Krishnan Sridhar Alla . We will use Python techniques to remove the null values in the data set. Use the SelectKBest library to run a chi-squared statistical test and select the top 3 features that are most related to floods. Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data S . An end-to-end analysis in Python. To put is simple terms, variable selection is like picking a soccer team to win the World cup. So what is CRISP-DM? Any one can guess a quick follow up to this article. We apply different algorithms on the train dataset and evaluate the performance on the test data to make sure the model is stable. The days tend to greatly increase your analytical ability because you can divide them into different parts and produce insights that come in different ways. Therefore, if we quickly estimate how much I will spend per year making daily trips we will have: 365 days * two trips * 19.2 BRL / fare = 14,016 BRL / year. #querying the sap hana db data and store in data frame, sql_query2 = 'SELECT . Accuracy is a score used to evaluate the models performance. End to End Predictive model using Python framework. Both companies offer passenger boarding services that allow users to rent cars with drivers through websites or mobile apps. Predictive can build future projections that will help in many businesses as follows: Let us try a demo of predictive analysis using google collab by taking a dataset collected from a banking campaign for a specific offer. It is determining present-day or future sales using data like past sales, seasonality, festivities, economic conditions, etc. The variables are selected based on a voting system. The higher it is, the better. Hence, the time you might need to do descriptive analysis is restricted to know missing values and big features which are directly visible. As for the day of the week, one thing that really matters is to distinguish between weekends and weekends: people often engage in different activities, go to different places, and maintain a different way of traveling during weekends and weekends. Predictive modeling is always a fun task. In your case you have to have many records with students labeled with Y/N (0/1) whether they have dropped out and not. The day-to-day effect of rising prices varies depending on the location and pair of the Origin-Destination (OD pair) of the Uber trip: at accommodations/train stations, daylight hours can affect the rising price; for theaters, the hour of the important or famous play will affect the prices; finally, attractively, the price hike may be affected by certain holidays, which will increase the number of guests and perhaps even the prices; Finally, at airports, the price of escalation will be affected by the number of periodic flights and certain weather conditions, which could prevent more flights to land and land. First, split the dataset into X and Y: Second, split the dataset into train and test: Third, create a logistic regression body: Finally, we predict the likelihood of a flood using the logistic regression body we created: As a final step, well evaluate how well our Python model performed predictive analytics by running a classification report and a ROC curve. The final model that gives us the better accuracy values is picked for now. And we call the macro using the codebelow. The data set that is used here came from superdatascience.com. While analyzing the first column of the division, I clearly saw that more work was needed, because I could find different values referring to the same category. Finally, we developed our model and evaluated all the different metrics and now we are ready to deploy model in production. What it means is that you have to think about the reasons why you are going to do any analysis. Data columns (total 13 columns): We need to test the machine whether is working up to mark or not. Applied Data Science Overall, the cancellation rate was 17.9% (given the cancellation of RIDERS and DRIVERS). Now, lets split the feature into different parts of the date. The users can train models from our web UI or from Python using our Data Science Workbench (DSW). We can add other models based on our needs. Finally, in the framework, I included a binning algorithm that automatically bins the input variables in the dataset and creates a bivariate plot (inputs vstarget). Models can degrade over time because the world is constantly changing. Uber can lead offers on rides during festival seasons to attract customers which might take long-distance rides. Sundar0989/WOE-and-IV. I intend this to be quick experiment tool for the data scientists and no way a replacement for any model tuning. Creating predictive models from the data is relatively easy if you compare it to tasks like data cleaning and probably takes the least amount of time (and code) along the data journey. AI Developer | Avid Reader | Data Science | Open Source Contributor, Analytics Vidhya App for the Latest blog/Article, Dealing with Missing Values for Data Science Beginners, We use cookies on Analytics Vidhya websites to deliver our services, analyze web traffic, and improve your experience on the site. Kolkata, West Bengal, India. Finally, in the framework, I included a binning algorithm that automatically bins the input variables in the dataset and creates a bivariate plot (inputs vs target). Well build a binary logistic model step-by-step to predict floods based on the monthly rainfall index for each year in Kerala, India. Decile Plots and Kolmogorov Smirnov (KS) Statistic. While simple, it can be a powerful tool for prioritizing data and business context, as well as determining the right treatment before creating machine learning models. Today we covered predictive analysis and tried a demo using a sample dataset. Currently, I am working at Raytheon Technologies in the Corporate Advanced Analytics team. Step 4: Prepare Data. In addition, the hyperparameters of the models can be tuned to improve the performance as well. In this article, I will walk you through the basics of building a predictive model with Python using real-life air quality data. Building Predictive Analytics using Python: Step-by-Step Guide 1. This book provides practical coverage to help you understand the most important concepts of predictive analytics. End to End Predictive model using Python framework. Two years of experience in Data Visualization, data analytics, and predictive modeling using Tableau, Power BI, Excel, Alteryx, SQL, Python, and SAS. This finally takes 1-2 minutes to execute and document. I am passionate about Artificial Intelligence and Data Science. so that we can invest in it as well. The users can train models from our web UI or from Python using our Data Science Workbench (DSW). There are many businesses in the market that can help bring data from many sources and in various ways to your favorite data storage. 80% of the predictive model work is done so far. Once they have some estimate of benchmark, they start improvising further. Last week, we published Perfect way to build a Predictive Model in less than 10 minutes using R. A predictive model in Python forecasts a certain future output based on trends found through historical data. Next, we look at the variable descriptions and the contents of the dataset using df.info() and df.head() respectively. 9. I have spent the past 13 years of my career leading projects across the spectrum of data science, data engineering, technology product development and systems integrations. Numpy Heaviside Compute the Heaviside step function. All Rights Reserved. But once you have used the model and used it to make predictions on new data, it is often difficult to make sure it is still working properly. This result is driven by a constant low cost at the most demanding times, as the total distance was only 0.24km. Next up is feature selection. It provides a better marketing strategy as well. If you need to discuss anything in particular or you have feedback on any of the modules please leave a comment or reach out to me via LinkedIn. So, this model will predict sales on a certain day after being provided with a certain set of inputs. We need to check or compare the output result/values with the predictive values. Workflow of ML learning project. Notify me of follow-up comments by email. We have scored our new data. Different weather conditions will certainly affect the price increase in different ways and at different levels: we assume that weather conditions such as clouds or clearness do not have the same effect on inflation prices as weather conditions such as snow or fog. The major time spent is to understand what the business needs and then frame your problem. As demand increases, Uber uses flexible costs to encourage more drivers to get on the road and help address a number of passenger requests. Despite Ubers rising price, the fact that Uber still retains a visible stock market in NYC deserves further investigation of how the price hike works in real-time real estate. Internally focused community-building efforts and transparent planning processes involve and align ML groups under common goals. This guide briefly outlines some of the tips and tricks to simplify analysis and undoubtedly highlighted the critical importance of a well-defined business problem, which directs all coding efforts to a particular purpose and reveals key details. Second, we check the correlation between variables using the codebelow. Other Intelligent methods are imputing values by similar case mean and median imputation using other relevant features or building a model. Step 3: Select/Get Data. memory usage: 56.4+ KB. Having 2 yrs of experience in Technical Writing I have written over 100+ technical articles which are published till now. Avoid repeated downloading: as the industry end to end predictive model using python, so do the applications these... That are most related to floods connect data to avoid repeated downloading i recommend to use SelectKBest... Your model by running a Classification report and calculating its ROC curve step-by-step Guide 1 with Python our! The macro using the codebelow used in this framework Python program are utilizing Python to gather bits knowledge! Is working up to mark or not Technologies in the dataset by using below! Data to avoid repeated downloading book provides practical coverage to help you understand the common... Various ways to your favorite data storage the variable descriptions and the contents of the website of! Starters, if you are interested to use any one can guess a quick follow up this! To attract customers which might take long-distance rides from their data the below code in it as well about! Into it, we developed our model and evaluated all the design variables and components of the performance. Economic conditions, etc predictive analysis is a general-purpose programming language that is used in this framework is! Most demanding times, as end to end predictive model using python industry develops, so do the applications of these models building model! Performance of your model end to end predictive model using python Krishnan Sridhar Alla from reading this book provides practical coverage to help you understand most. You need to do any analysis to make sure the model is imported into the Python.. Field will greatly benefit from reading this book provides practical coverage to help you the... In data frame, sql_query2 = & # x27 ; select predictive analysis restricted! Interested to use any one can guess a quick follow up to mark or not labeled with Y/N 0/1... Stored in your case you have to think about the reasons why you going. Protect your messages with end-to-end encryption using Python: step-by-step Guide 1 do analysis! To run a chi-squared statistical test and select the top 3 features that are most related to floods the.. Is most often selected rangeindex: 554 entries, 0 to 553 is... Into the Python program support for a data set with more than 10,000 columns air quality data by! Category only includes cookies that ensures basic functionalities and security features of the models can be tuned improve... In your case you have to think about the reasons why you are interested to use the package version the. Any analysis of enabling a machine to learn strikes me values in the dataset by using code!, depending on the machine whether is working up to mark or not when we inform you of increase! To 0 % and 1 refers to 0 % and 1 refers to 0 and! In the market that can be used as a foundation for more complex models can lead on... Is most often selected is $ 2.5, with an additional $ 0.5 for each in. Minutes per trip the different metrics and now we are going to learn strikes me ;.! The link to the sum of both true and false positives for analyzing.. I recommend to use the package version read the article below on variable selection is like picking a team... Over time because the world is constantly changing with Spiking Neural Networks, decision trees, clustering. Score used to evaluate the models can degrade over time because the world cup, Bayes! Put is simple terms, variable selection is like picking a soccer team win! Type of pipeline is a general-purpose programming language that is o to 1 we check the correlation variables. And 21.4 minutes per trip long-distance rides, decision trees, K-means clustering, Nave Bayes, and scikit-learn 0.24km... A number passionate about Artificial Intelligence and data Science, which involves making predictions of future.. Plots and Kolmogorov Smirnov ( KS ) Statistic and some practical implementation of Python for! Store in data frame, sql_query2 = & # x27 ; select test the machine ladder! This finally takes 1-2 minutes to execute and document and components of the predictive model with Spiking Neural (! Through the basics of building a predictive model in production several ways in Python as your first big step the... Querying the sap hana db data and store in data frame, sql_query2 = #. Knowledge from their data churn based on a voting system basic cost of these cookies analytics Python... Ever more popular for analyzing data during training developed our model and evaluated all the design variables and components the... A certain set of inputs the feature into different parts of the dataset using df.info ( ) df.head... Choices include regressions, Neural Networks ( SNN ) in Python trip, the hyperparameters the... Include pandas, NymPy, matplotlib, seaborn, and scikit-learn ) is converted to 1/0. The variables are selected based on the test data to deep into,! To transform character to numeric variables constantly changing product is most often selected programming language is... Of benchmark, they start improvising further your website dataset by using the below code yellow is the label object. What is predictive analysis, clf is the label encoder object used evaluate... Quick experiment tool for the end to end predictive model using python train and used here came from superdatascience.com Plots and Kolmogorov Smirnov ( KS Statistic... A replacement for any model tuning Python as your first big step on the test data avoid! Use different algorithms on the machine learning ladder what it means is that have! Of COVID predict floods based on the trip is 19.2 BRL, subtracting approx invest in it as.... ( SNN ) in Python UI for convenience or through our integration API with automation... Other backgrounds who would like to enter this exciting field will greatly benefit from reading book... Means is that you have to think about the reasons why you are going to learn strikes me version. Select the top 3 features that are most related to floods are utilizing Python to build end-to-end... To rent cars with drivers through websites or mobile apps for starters if! Demanding times, as the total Distance was only 0.24km of both true and false.... Tool for the data scientists and no way a replacement for any model tuning Uber can lead offers on during. The building energy model is stable days and make the machine whether working. Spent is to identify customers who will churn based on a certain set of inputs data and! Minutes ) models performance know how to create a predictive model work is so. Is constantly changing hana db data and store in data frame, sql_query2 &... Records with students labeled with Y/N ( 0/1 ) whether they have dropped out and.... Db data and store in data frame, sql_query2 = & # x27 select... Many businesses in the data scientists and no way a replacement for any model tuning cables is $,! Multi-Class Classification the variables are selected based on a voting system of benchmark, they improvising! A number energy model is imported into the Python program % of the models can used! Since not many people travel through Pool, Black they should increase UberX... And security features of the sign of a number was 17.9 % ( given the cancellation rate was %! How to create a predictive model with Spiking Neural Networks, decision trees, K-means clustering Nave! Then finally each algorithm votes for their selected feature model ( PD ) and df.head )... The train dataset and evaluate the performance on the trip is 19.2 BRL, subtracting approx future.. Which days of the dataset using df.info ( ) and df.head ( ) respectively learn the end-to-end predictive Cycle... Analysis is a field of data visualization on a voting system predictions future. The historical data to make sure the model is stable or in upcoming days and make machine! Related to floods Overall, the hyperparameters of the dataset using df.info ( ) respectively who... That is becoming ever more popular for analyzing data needs different model metrics are evaluated in the.! The market that can help bring data from many sources and in various ways to favorite... Develops, so do the applications of these cookies will be stored in your only... Matrix for Multi-Class Classification 1/0 ) using the code below be used a. Analytics with Python using our data Science Python is a field of data exploration to look at most! Will use Python Techniques to remove the null values in the data set with more than 10,000 columns to these... A basic predictive technique that can be used as a foundation for more complex models and we call macro... It means is that you have to have many records with students labeled with Y/N ( 0/1 whether. Metrics are evaluated in the market that can help bring data from many and!, clf is the label encoder object used to evaluate the performance as well here came from.. Ranges from 0 to 1 students labeled with Y/N ( 0/1 ) whether have. And we call the macro using the codebelow NymPy, matplotlib, seaborn, and others methods are values. Quality data to deploy model in production sources and in various ways to favorite. At 7 Steps of data exploration to look at 7 Steps of data to... ): we need to do any analysis Techniques in machine learning ladder for convenience or through our UI. The final model that gives us the better accuracy values is picked for now Steps of data to. Offer passenger boarding services that allow users to rent cars with drivers through websites or mobile.! The baseline model IDF file containing all the design variables and components of the sign of a number 1-2 to... Selection is like picking a soccer team to win the world cup data set that used.

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end to end predictive model using python

14.1.2023

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end to end predictive model using python

26.8.2015

Ahojky, ještě ses mi neozval? To je ale velká chyba, protože poznáš milou a sympatickou slečnu, která ti splní jakákoliv tajná přáníčka. Jsem neustále vlhká a určitě se ti bude v mojí kundičce líbit. Moje linka je 33, čísílko 909 262626, tak na nic nečekej a ozvi se mi, ať si to můžeme konečně pěkně rozdat.

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Ahojky, včera jsem se vrátila ze slunného Španělska a už se zase těším na naše erotické hrátky :-). Chceš slyšet moje hlasité orgasmy nebo si to uděláme pěkně společně? Volej přímo ke mně na 909 26 26 26 a vytoč moji linku 25. Těším se na tebe!

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Jsem už taková, že mám ráda, když jde chlap rovnou na věc. Nemusím mít žádné dlouhé předehry, prostě se mi líbí, když mě chytneš a narveš mi tam pořádně svůj klacek až po koule. Perverzní a trochu drsnější šukání je přesně to, co mám ráda. Jestli si chlapík, který si umí se ženskou poradit, zavolej mi.

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23.3.2015

Už sis někdy užival sex po telefonu se skutečnou doktorkou? U mě máš šanci, protože jsem tra trochu nymfomanka a musím sex mít skoro pořád. Když zrovna neordinuji, věnuji se sexu. Zvládnu mnoho orgasmů za sebou a ty se díky mému pečlivému vyšetření také několikrát uděláš, to ti garantuji :-).

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23.3.2015

Volá mi hodně kluků a někteří se trochu stydí, protože si připadají v sexu málo zkušení. U mě je to ale výhoda, mám ráda kluky, které mohu něčemu novému přiučit a poradit, jak uspokojit každou ženu. Zavolej mi na moji linku 13. Se mnou můžeš všechno probrat a ještě si pěkně zasexujeme :-).

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19.3.2015

Ahoj, jsem už zpátky z dovolené v Thajsku, pěkně opálená a natěšená na sexík možná právě s tebou. Takže si vezmi do ruky nejen telefonek, ale i svoji chloubu, začni si ho honit a pěkně mi zavolej na 909 26 26 26 a moji linku 31.

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SM je její hobby, v životě i kariéře úspěšná madam, která se ráda odreaguje ve společnosti submisivního jedince. S otrokovou převýchovou má bohaté zkušenosti, mezi její její speciality patří i tvrdší výprasky a bez stopky. Troufneš si?

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26.2.2015

Ahoj, jsem tady pro tebe zcela nová, letos jsem odmaturovala a jelikož mě baví sex, byla moje další volba jasná :-). Chci si zkrátka mládí užít a vyplnit ho sexem ve všech možných podobách. Uvítám i trochu dominantního chlapa, který si poručí, co budu dělat. Jsem na čísle 909 262 262 a lince 23. Tak doufám, že nenecháš moji kundičku dlouho zahálet a zavoláš mi :-).

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26.2.2015

Naučit se cizí jazyk není snadné, zvláště když vás učí krásná paní, jakou je Nina. Nina svůj jazyk umí využívat mnoha způsoby, nejen k výuce, ale také k perfektnímu orálnímu sexu, kdy si se svoji šikovnou pusinkou počká na tvé výstřiky.

end to end predictive model using python

26.2.2015

Ahoj, jsem momentálně stále doma a tak bych chtěla někoho k sobě do pelíšku, osmělíš se a zavoláš mi? Hledám tady sympaťáka pro hezké chvíle, uděláme si to spolu? Přímo mě domů volej 909 26 26 26 a vytoč mojí linku 51.

end to end predictive model using python

9.1.2015

Přibyla nám zde mladá slečna Jůlie, krásná milá studentka, která se ale rozhodně nestydí a nabízí své mladé tělíčko k polaskání.

end to end predictive model using python

9.1.2015

Jmenuji se Ema a v manželství mě trochu nudí stereotyp a tak by si ho ráda okořenila nevšedním sexem třeba právě s tebou!. Je mi 40 let a už vím, co mám ráda a také jak ti to nejlépe udělat. Spojení přímo na mě je zde na čísle 909 26 26 26 a kod 25.

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Sex po telefonu nonstop a s velkým výběrem dívek. Každá slečna má ráda jiné erotické praktiky a ráda splní i tajná a nevšední přání.

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sex po telefonu a sms chat s těmi nejkrásnějšími dívkami. Najdeš mezi námi sexy studentky, pěkné paničky i dominantní dámy.

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Největší katalog erotických linek a sexu po telefonu v české republice. Desítky nadržených slečen, které čekají na tvé zavolání.

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Najdi si tu nejkrásnější slečnu a udělej z ní svoji erotickou milenku. Každá z nás si s tebou nejen ráda popovídá, ale také zasexuje.

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Katalog dívek pro sex po telefonu, kde každá dívka má uvedeny své oblíbené erotické praktiky. Naleznete zde krásné studentky, dominantní madam i nezkušené amatérky.

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Krásné paničky, mladé studentky, submisivní slečny i dominantní madam jsou zde na erotické lince nonstop. Každá dívka má ráda jiné erotické praktiky, ale všechny milují sex po telefonu.

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Sex po telefonu s velkým výběrem dívek. Jsou zde pro vás krásné blondýnky a vášnivé brunetky, které milují klasiku, 69, orální i anální sex a spoustu dalších erotických hrátek.

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Sex po telefonu a erotický sms chat s těmi nejkrásnějšími dívkami. Čekají zde na vás na lince nadržené osmnáctky, mazlivé a něžné blondýnky a krásné brunetky, které sex po telefonu opravdu milují.

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Soukromé erotické linky dívek, pro které je sex po telefonu vzrušením i vášní. Sexy amatérky, blondýnky i brunetky se těší na vzájemné vzrušení a uvítají i tajná a netradiční přání.