Project is based on prediction of success of first stage of launch of Falcon 9 rocket by SpaceX. we can bid against SpaceX by using prediction and estimate the cost of launch.

Here I collected data from SpaceX websites through REST APIs, stored in data frame. I sorted data by filtering the specified columns useful for prediction. Data Wrangling done by calculating missing values, changing categorical values to dummy labels and numerical values to float. Exploratory data analysis done by SQL Queries and found unique launch sites . EDA through visualization plotted graphs on different attributes to check relation. From plots we can say increasing payload will decrease the chance of launch success and success is vary from different launch sites, orbit types. Created KPI Dashboards for all launch sites success rate, and also depends on payload. with this dashboard we can say in between 2k-6k there are more launches have successful outcome. we created folium map and marked all launch sites on it, to identify are they near to coastal, railway, city, highway. launch sites are near to coastal. now we evaluated different classification models and find their best parameters ,accuracy and best model among it. Among Decision Tree, SVM, KNN, Logistic Regression model , logistic regression model is the best model with highest accuracy of 83%.

So we can predict Falcon 9 rocket launch first stage will be 83% successful. We can bid against SpaceX by estimating cost of launch .

In this capstone, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

First we will collect and make sure the data is in the correct format from an API. Here we collected data from core,launchsite,payload,apis and saved it to pandas dataframe. we taken required colums and group by falcon9 rocket. we check null values in data frame. we replace payloadmass null values with its mean values and saved it to csv as dataset1.csv format.we performed web scraping to collect Falcon 9 historical launch records from a Wikipedia page titled List of Falcon 9 and Falcon Heavy launches

https://en.wikipedia.org/wiki/List_of_Falcon\_9\_and_Falcon_Heavy_launches we collected data through webscraping.using following link static_url = "https://en.wikipedia.org/w/index.php?title=List_of_Falcon_9_and_Falcon_Heavy_launches&oldid=1027686922" first using get we got response and created object using beautifulsoup. we extracted tables and created empty launch_dict and added all coluumns values and converted into dataframe and finally export it to csv file for furthey analysis. Next we performed some Exploratory Data Analysis (EDA) to find some patterns in the data and determine what would be the label for training supervised models.

In the data set, there are several different cases where the booster did not land successfully. Sometimes a landing was attempted but failed due to an accident; for example, True Ocean means the mission outcome was successfully landed to a specific region of the ocean while False Ocean means the mission outcome was unsuccessfully landed to a specific region of the ocean. True RTLS means the mission outcome was successfully landed to a ground pad False RTLS means the mission outcome was unsuccessfully landed to a ground pad.True ASDS means the mission outcome was successfully landed on a drone ship False ASDS means the mission outcome was unsuccessfully landed on a drone ship.

we mainly convert those outcomes into Training Labels with 1 means the booster successfully landed 0 means it was unsuccessful. Each launch aims to an dedicated orbit, and here are some common orbit types:

LEO: Low Earth orbit (LEO)is an Earth-centred orbit with an altitude of 2,000 km (1,200 mi) or less (approximately one-third of the radius of Earth),[1] or with at least 11.25 periods per day (an orbital period of 128 minutes or less) and an eccentricity less than 0.25.[2] Most of the manmade objects in outer space are in LEO [1].

VLEO: Very Low Earth Orbits (VLEO) can be defined as the orbits with a mean altitude below 450 km. Operating in these orbits can provide a number of benefits to Earth observation spacecraft as the spacecraft operates closer to the observation[2].

GTO A geosynchronous orbit is a high Earth orbit that allows satellites to match Earth's rotation. Located at 22,236 miles (35,786 kilometers) above Earth's equator, this position is a valuable spot for monitoring weather, communications and surveillance. Because the satellite orbits at the same speed that the Earth is turning, the satellite seems to stay in place over a single longitude, though it may drift north to south,” NASA wrote on its Earth Observatory website [3] .

SSO (or SO): It is a Sun-synchronous orbit also called a heliosynchronous orbit is a nearly polar orbit around a planet, in which the satellite passes over any given point of the planet's surface at the same local mean solar time [4] .

ES-L1 :At the Lagrange points the gravitational forces of the two large bodies cancel out in such a way that a small object placed in orbit there is in equilibrium relative to the center of mass of the large bodies. L1 is one such point between the sun and the earth [5] .

HEO A highly elliptical orbit, is an elliptic orbit with high eccentricity, usually referring to one around Earth [6].

ISS A modular space station (habitable artificial satellite) in low Earth orbit. It is a multinational collaborative project between five participating space agencies: NASA (United States), Roscosmos (Russia), JAXA (Japan), ESA (Europe), and CSA (Canada) [7]

MEO Geocentric orbits ranging in altitude from 2,000 km (1,200 mi) to just below geosynchronous orbit at 35,786 kilometers (22,236 mi). Also known as an intermediate circular orbit. These are "most commonly at 20,200 kilometers (12,600 mi), or 20,650 kilometers (12,830 mi), with an orbital period of 12 hours [8]

HEO Geocentric orbits above the altitude of geosynchronous orbit (35,786 km or 22,236 mi) [9]

GEO It is a circular geosynchronous orbit 35,786 kilometres (22,236 miles) above Earth's equator and following the direction of Earth's rotation [10]

PO It is one type of satellites in which a satellite passes above or nearly above both poles of the body being orbited (usually a planet such as the Earth [11] Here we calculated value counts of orbits,succces rate and we give dummy variables of 0,1 to launch succes and failure and saved it to csv file.Here we download dataset and manually upload into database by changing date format. Now by using Db2 credentials we connect link to database from jupyter notebook. then we run sql quieries to solve problems.