By using the data given in the attached CSV file:---Write python code to read data from that CSV file#### Normalize numeric columns using at least two normalization techniques in data science. Use python.#### Apply at least 5 missing values on any one of the columns then fill with any of the filling techniques mentioned above(except mean() filling)#### show scatter plot of numeric columns against each other, apply labelings#### get manhattan and euclidean distance matrix for numeric columns,#### choose any observation and explain who is the closest observation for him#### describe briefly about the type of States column. what kind of data is that column#### explain which column has the highest correaltion against profit after R& D Spendings
R&D Spend,Administration,Marketing Spend,State,Profit 165349.2,136897.8,471784.1,New York,192261.83 162597.7,151377.59,443898.53,California,191792.06 153441.51,101145.55,407934.54,Florida,191050.39 144372.41,118671.85,383199.62,New York,182901.99 142107.34,91391.77,366168.42,Florida,166187.94 131876.9,99814.71,362861.36 t,New York,156991.12 134615.46,147198.87,127716.82,California,156122.51 130298.13,145530.06,323876.68,Florida,155752.6 120542.52,148718.95,311613.29,New York,152211.77 123334.88,108679.17,304981.62,California,149759.96 101913.08,110594.11,229160.95,Florida,146121.95 100671.96,91790.61,249744.55,California,144259.4 93863.75,127320.38,249839.44,Florida,141585.52 91992.39,135495.07,252664.93,California,134307.35 119943.24,156547.42,256512.92 r,Florida,132602.65 114523.61,122616.84,261776.23,New York,129917.04 78013.11,121597.55,264346.06,California,126992.93 94657.16,145077.58,282574.31,New York,125370.37 91749.16,114175.79,294919.57,Florida,124266.9 86419.7,153514.11,0,New York,122776.86 76253.86,113867.3,298664.47,California,118474.03 78389.47,153773.43,299737.29,New York,111313.02 73994.56,122782.75,303319.26,Florida,110352.25 67532.53,105751.03,304768.73,Florida,108733.99 77044.01,99281.34,140574.81,New York,108552.04 64664.71,139553.16,137962.62,California,107404.34 75328.87,144135.98,134050.07,Florida,105733.54 72107.6,127864.55,353183.81,New York,105008.31 66051.52,182645.56,118148.2,Florida,103282.38 65605.48,153032.06,107138.38,New York,101004.64 61994.48,115641.28,91131.24,Florida,99937.59 61136.38,152701.92,88218.23,New York,97483.56 63408.86,129219.61,46085.25,California,97427.84 55493.95,103057.49,214634.81,Florida,96778.92 46426.07,157693.92,210797.67,California,96712.8 46014.02,85047.44,205517.64,New York,96479.51 28663.76,127056.21,201126.82,Florida,90708.19 44069.95,51283.14,197029.42,California,89949.14 20229.59,65947.93,185265.1,New York,81229.06 38558.51,82982.09,174999.3,California,81005.76 28754.33,118546.05,172795.67,California,78239.91 27892.92,84710.77,164470.71,Florida,77798.83 23640.93,96189.63,148001.11,California,71498.49 15505.73,127382.3,35534.17,New York,69758.98 22177.74,154806.14,28334.72,California,65200.33 1000.23,124153.04,1903.93,New York,64926.08 1315.46,115816.21,297114.46,Florida,49490.75 0,135426.92,0,California,42559.73 542.05,51743.15,0,New York,35673.41 0,116983.8,45173.06,California,14681.4