As you know we have a project in this class that is worth 20 points.The project is due 12/21/2020 @ 11:59 pm. The project allows you to demonstrate your knowledge of Python and your ability to...

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As you know we have a project in this class that is worth 20 points.The project is due 12/21/2020 @ 11:59 pm.


The project allows you to demonstrate your knowledge of Python and your ability to construct programs that facilitate the analysis of data.I have provided the file of data you are to analyze (vehicles.csv).


I have released:


When all will be done please send me all files with clear names that I will know what is what, also I put the deadlinelittle bit earlier to just have timefor myself to check it and send it to my teacher

ALSO PLEASE MAKE SURE THAT IF YOU WILL SEND ME THIS CODES I WILL BE ABLE SEND IT RIGHT AWAY TO MY TEACHER TO NOT DO ENYTHING MORE JUST DANWLOAD AND SEND IT FORWARD TO MY TEACHER


Also please give those assignmentsto Sudipta Expert i like the work and he knows what to do


If you have any questions please feel free to text me




CTIM285_Fall_2020_Project_11_27_2020 CTIM285: Fall 2020: FINAL PROJECT (20 Points) Prof Brown-Sederberg DUE (viaCanvas: Tuesday, December 21, 2020 @ 11:59 PM Project Description: You have been hired by the State of Happiness (SOH) to analyze traffic statistic from the state’s major highway, the Road to Oblivion (RTO). You will be analyzing west bound traffic only. Tolls are collected through an automated billing system that uses electronic transponders associated with specific vehicles. You will be using the data gathered through the transponders for your analysis. The RTO is a 210 mile toll road with tolls calculated on the distance traveled. The speed limit on the highway is 70 miles per hour. There are 9 segments to the highway. Vehicles may enter or exit at any of the 10 entrances/exits. All entrances onto and exits from the RTO have electronic sensors that note the time of the vehicle entrance onto the RTO and exit from the RTO. That information is saved in a file associated with the vehicle. The time at which vehicles traveling on the RTO passes an exit, but do not exit the RTO, is also noted and that data also saved in a file associated with the vehicle. Time is recorded by the minute of the day (e.g. 1:00 am is 60, 11:59 pm is 1439). The entrances and exits of the highway are as follows: Entrance/Exit 0 @ mile 0 Entrance/Exit 1 @ mile 20 Entrance/Exit 2 @ mile 45 Entrance/Exit 3 @ mile 60 Entrance/Exit 4 @ mile 70 Entrance/Exit 5 @ mile 100 Entrance/Exit 6 @ mile 130 Entrance/Exit 7 @ mile 155 Entrance/Exit 8 @ mile 190 Entrance/Exit 9 @ mile 210 The segments are: Segment 1: Entrance/Exit 0 to Entrance/Exit 1 Segment 2: Entrance/Exit 1 to Entrance/Exit 2 Segment 3: Entrance/Exit 2 to Entrance/Exit 3 Segment 4: Entrance/Exit 3 to Entrance/Exit 4 Segment 5: Entrance/Exit 4 to Entrance/Exit 5 Segment 6: Entrance/Exit 5 to Entrance/Exit 6 Segment 7: Entrance/Exit 6 to Entrance/Exit 7 Segment 8: Entrance/Exit 7 to Entrance/Exit 8 Segment 9: Entrance/Exit 8 to Entrance/Exit 9 Data for Analysis: The attached file is the RTO vehicle data collected by the transponders as described above. Each line of the attached file represents a single car. Each line contains 10 values which represent the 10 entrance/exit points on the RTO. The values represent the time (in minutes, starting at midnight) when the vehicle passed that point. The minimum time is 0 and the maximum time is 1439. The value of -1 indicates that the car did not pass that point (i.e. it was not on the RTO). The first non -1 value represents the time at which the vehicle entered the RTO, the last non -1 value represents the time at which the car exited the RTO. Please note that when reading the file vehicles.csv the lines are read as lists of Strings, if you want to work with ints and not Strings you may use the following code to convert each line read (line) into a list of ints (times) in order to make it easier to work with the data: import csv with open ('vehicles.csv', mode='r',newline='') as input_stream: reader = csv.reader(input_stream) for line in reader: times = [] for i in line: times.append(int(i)) Information Requests: The SOH has has asked you to provide the following information (using as input data the attached file as noted above): 1. The average speed of vehicles for the entirely of their travel on the RTO. 2. The number of vehicles exceeding the speed limit for the entirely of their travel on the RTO. 3. The percentage of vehicles exceeding the speed limit for the entirely of their travel on the RTO. 4. The number of vehicles not exceeding the speed limit for the entirely of their travel on the RTO. 5. The percentage of vehicles not exceeding the speed limit for the entirely of their travel on the RTO. 6. The average speed of vehicles in each segment of the RTO. 7. The number of vehicles exceeding the speed limit in each segment of the RTO. 8. The percentage of vehicles exceeding the speed limit in each segment of the RTO . 9. The number of vehicles not exceeding the speed limit in each segment of the RTO. 10. The percentage of vehicles not exceeding the speed limit in each segment of the RTO. Program: Write a program using the Python language and the IDE Jupyter to generate the information requested above and the graphs noted below. Form of Report of Results: You are to write a report (minimum 2 pages, not including graphs), double spaced, Times New Roman, 12 point, 1 inch margins) summarizing your analysis and results. Your report must include: A. Description of Analysis: Explain how your program processed the data to perform the analysis of the data. Comparisons, calculations etc. B. Description of Code: What were the decisions you made in the design of the code you developed to perform and display the analysis. C. Graphs: Develop graphs (required graphs below) that portray the results (required results below) of your analysis of the data. The graphs should be clear, self explanatory and accurate portrayals of the results of your analysis. You must have a minimum of 5 graphs. You must use at least 3 different forms of graph. The graphs are not to be included in the 2 page minimum, I suggest you include them in an appendix. D. Results: Explain the results of your analysis of the data. The form of the results that are expected for each specified information request is noted in bold. 1. The average speed of vehicles for the entirely of their travel on the RTO. Expected: value, with appropriate explanation. 2. The number of vehicles exceeding the speed limit for the entirely of their travel on the RTO. Expected: value, with appropriate explanation. 3. The percentage of vehicles exceeding the speed limit for the entirely of their travel on the RTO.Expected: value, with appropriate explanation. 4. The number of vehicles not exceeding the speed limit for the entirely of their travel on the RTO. Expected: value, with appropriate explanation. 5. The percentage of vehicles not exceeding the speed limit for the entirely of their travel on the RTO. Expected: value, with appropriate explanation. 6. The average speed of vehicles in each segment of the RTO. Expected: values and graph, with appropriate explanation. 7. The number of vehicles exceeding the speed limit in each segment of the RTO. Expected: values and graph, with appropriate explanation. 8. The percentage of vehicles exceeding the speed limit in each segment of the RTO. Expected: values and graph, with appropriate explanation. 9. The number of vehicles not exceeding the speed limit in each segment of the RTO. Expected: values and graph, with appropriate explanation. 10. The percentage of vehicles not exceeding the speed limit in each segment of the RTO. Expected: values and graph, with appropriate explanation. Required Submission Files: 1. Report (Word or Pages file) 2. Code (.ipynb file(s)) DUE (via Canvas): Tuesday, December 21, 2020 @ 11:59 PM
Answered Same DayNov 28, 2021

Answer To: As you know we have a project in this class that is worth 20 points.The project is due 12/21/2020 @...

Sudipta answered on Dec 15 2021
163 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The average speed of vehicles for the entirely of their travel on the RTO: 71.77\n",
"The number of vehicles exceeding the speed limit for the entirely of their travel on the RTO: 536\n",
"The percentage of vehicles exceeding the speed limit for the entirely of their travel on the RTO: 53.6\n",
"The number of vehicles not exceeding the speed limit for the entirely of their travel on the RTO: 464\n",
"The percentage of vehicles not exceeding the speed limit for the entirely of theirtravel on the RTO: 46.4\n",
"The average speed of vehicles in segment 1: 8.98\n",
"The average speed of vehicles in segment 2: 14.33\n",
"The average speed of vehicles in segment 3: 22.63\n",
"The average speed of vehicles in segment 4: 29.46\n",
"The average speed of vehicles in segment 5: 32.42\n",
"The average speed of vehicles in segment 6: 37.8\n",
"The average speed of vehicles in segment 7: 36.44\n",
"The average speed of vehicles in segment 8: 33.5\n",
"The average speed of vehicles in segment 9: 25.08\n",
"The number of vehicles exceeding the speed limit in each segment: [71, 101, 164, 217, 241, 285, 265, 245, 191]\n",
"The percentage of vehicles exceeding the speed limit in each segment of the RTO: [35.5, 34.59, 43.27, 47.28, 45.39, 50.35, 47.92, 47.3, 61.81]\n",
"The number of vehicles not exceeding the speed limit in each segment: [129, 191, 215, 242, 290, 281, 288, 273, 118]\n",
"The percentage of vehicles not exceeding the speed limit in each segment of the RTO: [64.5, 65.41, 56.73, 52.72, 54.61, 49.65, 52.08, 52.7, 38.19]\n"
]
}
],
"source": [
"import csv\n",
"with open('vehicles.csv',mode='r',newline='') as input_stream:\n",
" reader=csv.reader(input_stream)\n",
" tot_spd=0\n",
" vehcl=0\n",
" blw_lmt=0\n",
" abv_lmt=0\n",
" sgmnt1=[]\n",
" sgmnt2=[]\n",
" sgmnt3=[]\n",
" sgmnt4=[]\n",
" sgmnt5=[]\n",
" sgmnt6=[]\n",
" sgmnt7=[]\n",
" sgmnt8=[]\n",
" sgmnt9=[]\n",
" blw_lmt1=0\n",
" abv_lmt1=0\n",
" blw_lmt2=0\n",
" abv_lmt2=0\n",
" blw_lmt3=0\n",
" abv_lmt3=0\n",
" blw_lmt4=0\n",
" abv_lmt4=0\n",
" blw_lmt5=0\n",
" abv_lmt5=0\n",
" blw_lmt6=0\n",
" abv_lmt6=0\n",
" blw_lmt7=0\n",
" abv_lmt7=0\n",
" blw_lmt8=0\n",
" abv_lmt8=0\n",
" blw_lmt9=0\n",
" abv_lmt9=0\n",
" sgmnt_grph=[]\n",
" abv_lmtgrph=[]\n",
" blw_lmtgrph=[]\n",
" for line in reader:\n",
" times=[]\n",
" vehcl+=1\n",
" d={0:0,1:20,2:45,3:60,4:70,5:100,6:130,7:155,8:190,9:210}\n",
" for i in line:\n",
" times.append(int(i))\n",
" for i in range(len(times)):\n",
" if times[i]>=0:\n",
" strt=i\n",
" for j in range(i+1,len(times)):\n",
" if times[j]>=0:\n",
" end=j \n",
" break\n",
" for i in range(1,len(times)):\n",
" if times[i]==-1 and times[i-1]>0:\n",
" times[i]=times[i-1]\n",
" if times[i]>=0 and times[i-1]==-1:\n",
" times[i-1]=0\n",
" if times[i]-times[i-1]>0:\n",
" val=((d[i]-d[i-1])/(times[i]-times[i-1]))*60\n",
" if i==1:\n",
" sgmnt1.append(val)\n",
" if val>70:\n",
" abv_lmt1+=1\n",
" else:\n",
" blw_lmt1+=1\n",
" elif i==2:\n",
" sgmnt2.append(val)\n",
" if val>70:\n",
" abv_lmt2+=1\n",
" else:\n",
" blw_lmt2+=1\n",
" elif i==3:\n",
" sgmnt3.append(val)\n",
" if val>70:\n",
" abv_lmt3+=1\n",
" else:\n",
" blw_lmt3+=1\n",
" elif i==4:\n",
" sgmnt4.append(val)\n",
" if val>70:\n",
" abv_lmt4+=1\n",
" else:\n",
" blw_lmt4+=1\n",
" elif i==5:\n",
" sgmnt5.append(val)\n",
" if val>70:\n",
" abv_lmt5+=1\n",
" else:\n",
" blw_lmt5+=1\n",
" elif i==6:\n",
" sgmnt6.append(val)\n",
" if val>70:\n",
" abv_lmt6+=1\n",
" else:\n",
" blw_lmt6+=1\n",
" elif i==7:\n",
" sgmnt7.append(val)\n",
" if val>70:\n",
" abv_lmt7+=1\n",
" else:\n",
" blw_lmt7+=1\n",
" elif i==8:\n",
" sgmnt8.append(val)\n",
" if val>70:\n",
" abv_lmt8+=1\n",
" else:\n",
" blw_lmt8+=1\n",
" elif i==9:\n",
" sgmnt9.append(val)\n",
" if val>70:\n",
" abv_lmt9+=1\n",
" else:\n",
" blw_lmt9+=1\n",
" value=times[end]-times[strt]\n",
" dst=d[end]-d[strt]\n",
" spd=(round((dst/value)*60,2))\n",
" if spd>70:\n",
" abv_lmt+=1\n",
" else:\n",
" blw_lmt+=1\n",
" tot_spd+=spd\n",
" avg_spd=tot_spd/vehcl\n",
" abv_lmtgrph.append(abv_lmt1)\n",
" abv_lmtgrph.append(abv_lmt2)\n",
" abv_lmtgrph.append(abv_lmt3)\n",
" abv_lmtgrph.append(abv_lmt4)\n",
" abv_lmtgrph.append(abv_lmt5)\n",
" abv_lmtgrph.append(abv_lmt6)\n",
" abv_lmtgrph.append(abv_lmt7)\n",
" abv_lmtgrph.append(abv_lmt8)\n",
" abv_lmtgrph.append(abv_lmt9)\n",
" blw_lmtgrph.append(blw_lmt1)\n",
" blw_lmtgrph.append(blw_lmt2)\n",
" blw_lmtgrph.append(blw_lmt3)\n",
" blw_lmtgrph.append(blw_lmt4)\n",
" blw_lmtgrph.append(blw_lmt5)\n",
" blw_lmtgrph.append(blw_lmt6)\n",
" blw_lmtgrph.append(blw_lmt7)\n",
" blw_lmtgrph.append(blw_lmt8)\n",
" blw_lmtgrph.append(blw_lmt9)\n",
" prcnt=[0,0,0,0,0,0,0,0,0]\n",
" abv_prcnt=[0,0,0,0,0,0,0,0,0]\n",
" blw_prcnt=[0,0,0,0,0,0,0,0,0]\n",
" for i in range(9):\n",
" prcnt[i]=abv_lmtgrph[i]+blw_lmtgrph[i]\n",
" abv_prcnt[i]=round((abv_lmtgrph[i]/prcnt[i])*100,2)\n",
" blw_prcnt[i]=round((blw_lmtgrph[i]/prcnt[i])*100,2)\n",
" print(\"The average speed of vehicles for the entirely of their travel on the RTO:\",round(avg_spd,2))\n",
" print(\"The number of vehicles exceeding the speed limit for the entirely of their travel on the RTO:\",abv_lmt)\n",
" print(\"The percentage of vehicles exceeding the speed limit for the entirely of their travel on the RTO:\",(abv_lmt*100)/vehcl)\n",
" print(\"The number of vehicles not exceeding the speed limit for the entirely of their travel on the RTO:\",blw_lmt)\n",
" print(\"The percentage of vehicles not exceeding the speed limit for the entirely of theirtravel on the RTO:\",(blw_lmt*100)/vehcl)\n",
" print(\"The average speed of vehicles in segment 1:\",round(sum(sgmnt1)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt1)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 2:\",round(sum(sgmnt2)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt2)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 3:\",round(sum(sgmnt3)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt3)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 4:\",round(sum(sgmnt4)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt4)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 5:\",round(sum(sgmnt5)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt5)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 6:\",round(sum(sgmnt6)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt6)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 7:\",round(sum(sgmnt7)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt7)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 8:\",round(sum(sgmnt8)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt8)/vehcl,2))\n",
" print(\"The average speed of vehicles in segment 9:\",round(sum(sgmnt9)/vehcl,2))\n",
" sgmnt_grph.append(round(sum(sgmnt9)/vehcl,2))\n",
" print(\"The number of vehicles exceeding the speed limit in each segment:\",abv_lmtgrph)\n",
" print(\"The percentage of vehicles exceeding the speed limit in each segment of the RTO:\",abv_prcnt)\n",
" print(\"The number of vehicles not exceeding the speed limit in each segment:\",blw_lmtgrph)\n",
" print(\"The percentage of vehicles not exceeding the speed limit in each segment of the RTO:\",blw_prcnt)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Vw93a0Cz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\nUn5CA10ppfyEBrpSSvkJDXSllPIT/w/L9Pmoa5I7FAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.title(\"Average vehicle speed of each Segment\")\n",
"plt.plot(sgmnt_grph)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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1WiGGH+TmOS0drDizgXEetDT0dkf2t2NcjZ6LL22b7yI1MZausZp28a8Q+IXv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\nSvQNo3d9Lr44cilxuJmxC70qvDIr6iWxgTozlbHWZXtM1I2Mp3bXjJX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YYYH5znQ70U6e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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.title(\"Average vehicle speed of Segment1\")\n",
"plt.plot(sgmnt1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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l/wARPQYA3LekmrwXwBPUcY932zZcin72ExhoF/vWvJBm15PzFvqVV1abLeNS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YQm21lsXsL1rCiaqgIipjRQeAwBGZWNLWc/x4Die8xKhBaRG9+np3EMZ88Ro7GtelIKIftey/cn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\n/9EAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.title(\"Average vehicle speed of Segment2\")\n",
"plt.plot(sgmnt2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png":...
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