diff --git a/Nifty/FMCG-Stock-Price-Prediction/FMCG_20companies.csv b/Nifty/FMCG-Stock-Price-Prediction/FMCG_20companies.csv new file mode 100644 index 00000000..7a3daea6 --- /dev/null +++ b/Nifty/FMCG-Stock-Price-Prediction/FMCG_20companies.csv @@ -0,0 +1,481 @@ +Quarters,Company,Oil Price,Total Income From Operations,Return on Equity Ratio,Price-Equity Ratio,Gross Margin,Profit Margin,EPS,EV/Net Operating Revenue,Change in Inventory,CPI,Closing Stock Price +Mar '20,Procter and Gamble,2392.98,656.05,20.21,146.39,0.64,0.14,28.07,52.15,-16.18,148.6,4109.05 +Dec '19,Procter and Gamble,4509.77,859.27,26.47,103.92,0.65,0.16,41.88,39.81,21.2,150.4,4352.3 +Sep '19,Procter and Gamble,4282.87,852.14,26.25,111.13,0.59,0.16,42.16,40.15,-8.62,145.8,4685.15 +Jun '19,Procter and Gamble,4149.67,637.29,19.63,219.22,0.55,0.1,18.73,53.68,-25.4,142.9,4106.05 +Mar '19,Procter and Gamble,4432.04,699.34,21.54,132.34,0.61,0.13,27.76,45.39,25.8,140.4,3673.65 +Dec '18,Procter and Gamble,3822.07,818.07,25.2,80.65,0.57,0.15,38.24,38.8,11.46,140.1,3084.2 +Sep '18,Procter and Gamble,5448.55,791.8,24.39,58.08,0.54,0.18,44.4,40.09,-49.59,140.2,2578.9 +Jun '18,Procter and Gamble,4879.75,524.65,16.16,172.72,0.63,0.08,13.72,60.5,-6.56,138.5,2369.7 +Mar '18,Procter and Gamble,4171.72,568.89,17.53,58.68,0.67,0.15,25.64,45.71,15.73,136.5,1504.65 +Dec '17,Procter and Gamble,3930.99,704.16,21.69,31.87,0.62,0.19,40.43,36.93,9.76,137.2,1288.65 +Sep '17,Procter and Gamble,3413.19,657.59,20.26,29.21,0.64,0.18,35.6,39.54,42.71,135.2,1040.05 +Jun '17,Procter and Gamble,2975.01,502.87,15.49,44.35,0.55,0.16,24.04,51.71,-34.77,132,1066.25 +Mar '17,Procter and Gamble,3355.09,573.87,17.68,33.92,0.59,0.17,30.69,33.64,3.9,130.9,1041.05 +Dec '16,Procter and Gamble,3572.84,643.24,19.82,21.5,0.59,0.23,46.4,30.01,-29.81,130.4,997.5 +Sep '16,Procter and Gamble,3006.05,600.42,18.5,21.81,0.6,0.17,32.17,32.15,9.57,130.9,701.5 +Jun '16,Procter and Gamble,3208.66,558.27,17.2,20.92,0.63,0.2,33.7,34.58,-18.67,130.1,704.85 +Mar '16,Procter and Gamble,2503.95,613.8,18.91,21.44,0.63,0.16,29.98,34.81,1.98,126,642.75 +Dec '15,Procter and Gamble,2435.4,713.7,21.99,17.24,0.66,0.21,45.19,29.94,26.83,126.1,779 +Sep '15,Procter and Gamble,3064.43,598.56,18.44,36.43,0.58,0.12,21.5,35.7,-20.85,125.4,783.25 +Jun '15,Procter and Gamble,3915.3,557.56,17.18,24.27,0.62,0.19,32.99,38.32,-13.4,123,800.75 +Mar '15,Procter and Gamble,3299.23,555.23,17.11,34.07,0.86,0.16,26.77,24.42,22.85,120.2,912 +Dec '14,Procter and Gamble,3806.55,644.51,19.86,28.46,0.86,0.14,27.93,21.03,8.77,119.4,794.8 +Sep '14,Procter and Gamble,5835.69,576.49,17.76,44.8,0.55,0.11,18.95,23.51,-19.46,120.1,848.95 +Jun '14,Procter and Gamble,6471.05,486.1,14.98,26.85,0.61,0.18,27.7,27.89,-17.24,116.7,743.75 +Mar '20,Tata Consumer Products,2392.98,1335.31,14.49,477.86,0.39,0.12,0.77,19.82,-26.4,148.6,367.95 +Dec '19,Tata Consumer Products,4509.77,1463.45,15.88,207.13,0.38,0.1,1.55,18.08,-10.28,150.4,321.05 +Sep '19,Tata Consumer Products,4282.87,914.14,14.48,174.72,0.39,0.11,1.58,28.95,-3.59,145.8,276.05 +Jun '19,Tata Consumer Products,4149.67,968.82,15.35,160.54,0.44,0.13,1.68,27.32,66.03,142.9,269.7 +Mar '19,Tata Consumer Products,4432.04,784.05,12.42,241.85,0.35,0.07,0.84,15.75,-30.85,140.4,203.15 +Dec '18,Tata Consumer Products,3822.07,889.73,14.1,154.51,0.36,0.1,1.42,13.88,-27.59,140.1,219.4 +Sep '18,Tata Consumer Products,5448.55,847.28,13.43,122.74,0.36,0.14,1.9,14.58,-19.96,140.2,233.2 +Jun '18,Tata Consumer Products,4879.75,908.6,14.4,114.66,0.49,0.16,2.35,13.59,78.56,138.5,269.45 +Mar '18,Tata Consumer Products,4171.72,714.65,11.32,307.68,0.35,0.09,0.84,22.19,-49.93,136.5,258.45 +Dec '17,Tata Consumer Products,3930.99,848.43,13.44,95.62,0.39,0.01,3.3,18.69,-20.02,137.2,315.55 +Sep '17,Tata Consumer Products,3413.19,794.8,12.59,108.24,0.42,0.15,1.9,19.95,20.55,135.2,205.65 +Jun '17,Tata Consumer Products,2975.01,859.44,13.62,62.21,0.46,0.13,2.42,18.45,47.73,132,150.55 +Mar '17,Tata Consumer Products,3355.09,696.57,11.04,214.64,0.32,0.06,0.7,13.63,-60.98,130.9,150.25 +Dec '16,Tata Consumer Products,3572.84,786.43,12.46,106.13,0.38,0.09,1.15,12.07,-10.48,130.4,122.05 +Sep '16,Tata Consumer Products,3006.05,759.12,12.03,101.82,0.39,0.11,1.37,12.51,3.31,130.9,139.5 +Jun '16,Tata Consumer Products,3208.66,820.08,12.99,112.8,0.43,0.09,1.16,11.58,66.36,130.1,130.85 +Mar '16,Tata Consumer Products,2503.95,676.75,10.72,1210,0.34,0.18,0.1,11.01,-32.32,126,121 +Dec '15,Tata Consumer Products,2435.4,761.1,12.06,187.31,0.34,0.07,0.78,9.79,-21.1,126.1,146.1 +Sep '15,Tata Consumer Products,3064.43,748.23,11.86,76.47,0.37,0.15,1.67,9.96,8.06,125.4,127.7 +Jun '15,Tata Consumer Products,3915.3,802.32,12.71,128.5,0.4,0.08,1.03,9.29,41.44,123,132.35 +Mar '15,Tata Consumer Products,3299.23,671.49,10.64,219.26,0.31,0.27,0.68,14.41,-62.56,120.2,149.1 +Dec '14,Tata Consumer Products,3806.55,781.75,12.64,167.44,0.36,0.07,0.9,12.38,-0.65,119.4,150.7 +Sep '14,Tata Consumer Products,5835.69,712.37,11.52,75.05,0.31,0.19,2.12,13.58,-16.94,120.1,159.1 +Jun '14,Tata Consumer Products,6471.05,726.05,11.74,180,0.42,0.08,0.96,13.33,52.16,116.7,172.8 +Mar '20,United Breweries Ltd.,2392.98,1424.17,53.86,613.24,0.5,0.03,1.56,17.1,-10.41,148.6,956.65 +Dec '19,United Breweries Ltd.,4509.77,1453.28,54.97,316.47,0.53,0.07,4.02,16.76,1.26,150.4,1272.2 +Sep '19,United Breweries Ltd.,4282.87,1578.57,59.7,308.96,0.52,0.07,4.36,15.43,5.42,145.8,1347.05 +Jun '19,United Breweries Ltd.,4149.67,2048.53,77.48,215.29,0.5,0.08,6.22,11.89,-5.82,142.9,1339.1 +Mar '19,United Breweries Ltd.,4432.04,1629.4,61.63,542.86,0.49,0.04,2.57,22.66,-27.56,140.4,1395.15 +Dec '18,United Breweries Ltd.,3822.07,1451.17,54.89,333.35,0.56,0.08,4.13,25.44,40.3,140.1,1376.75 +Sep '18,United Breweries Ltd.,5448.55,1525.95,57.71,219.88,0.54,0.11,6.2,24.2,-23.12,140.2,1363.25 +Jun '18,United Breweries Ltd.,4879.75,1865.91,70.57,137.41,0.51,0.12,8.39,19.79,-64.09,138.5,1152.9 +Mar '18,United Breweries Ltd.,4171.72,1469.28,55.57,275.33,0.52,0.06,3.44,17.21,-17.2,136.5,947.15 +Dec '17,United Breweries Ltd.,3930.99,1197.1,45.28,604.22,0.53,0.04,1.79,21.12,6.01,137.2,1081.55 +Sep '17,United Breweries Ltd.,3413.19,1276.44,48.28,231.38,0.55,0.07,3.55,19.81,6.07,135.2,821.4 +Jun '17,United Breweries Ltd.,2975.01,1674.21,63.32,127.48,0.54,0.1,6.12,15.1,27.98,132,780.15 +Mar '17,United Breweries Ltd.,3355.09,1112.7,42.08,3082.2,0.5,0.01,0.25,18.65,-19.69,130.9,770.55 +Dec '16,United Breweries Ltd.,3572.84,1025.02,38.77,424.73,0.52,0.05,1.84,20.24,-12.04,130.4,781.5 +Sep '16,United Breweries Ltd.,3006.05,1038.55,39.28,884.51,0.55,0.03,1.02,19.98,9.33,130.9,902.2 +Jun '16,United Breweries Ltd.,3208.66,1562.32,59.09,135.09,0.54,0.09,5.56,13.28,-7.44,130.1,751.1 +Mar '16,United Breweries Ltd.,2503.95,1212.49,45.86,416.16,0.52,0.04,1.98,18.3,-23.71,126,824 +Dec '15,United Breweries Ltd.,2435.4,1096.91,41.49,350.59,0.58,0.06,2.7,20.23,-4.92,126.1,946.6 +Sep '15,United Breweries Ltd.,3064.43,1072.27,40.55,447.46,0.58,0.05,1.97,20.69,12.14,125.4,881.5 +Jun '15,United Breweries Ltd.,3915.3,1452.84,54.95,201.83,0.55,0.08,4.62,15.27,5.42,123,932.45 +Mar '15,United Breweries Ltd.,3299.23,1161.62,43.93,560.06,0.56,0.04,1.79,23.41,-28.18,120.2,1002.5 +Dec '14,United Breweries Ltd.,3806.55,1000.42,37.84,563.45,0.61,0.04,1.48,27.18,2.69,119.4,833.9 +Sep '14,United Breweries Ltd.,5835.69,1085.67,41.06,447.55,0.6,0.04,1.59,25.05,0.74,120.1,711.6 +Jun '14,United Breweries Ltd.,6471.05,1444.58,54.64,144.11,0.59,0.09,4.85,18.83,9.1,116.7,698.95 +Mar '20,United Spirits Ltd,2392.98,1993.8,13.72,1800.76,0.41,0.01,0.33,18.32,-23.6,148.6,594.25 +Dec '19,United Spirits Ltd,4509.77,2582.5,17.77,168.36,0.52,0.1,3.56,14.14,198.8,150.4,599.35 +Sep '19,United Spirits Ltd,4282.87,2296.2,15.8,215.86,0.38,0.1,3.09,15.91,-156.6,145.8,667 +Jun '19,United Spirits Ltd,4149.67,2218.4,15.27,214.96,0.51,0.09,2.72,16.47,88.8,142.9,584.7 +Mar '19,United Spirits Ltd,4432.04,2250,15.49,319.65,0.47,0.06,1.73,18.97,19.9,140.4,553 +Dec '18,United Spirits Ltd,3822.07,2500.9,17.21,239.55,0.49,0.09,2.65,17.07,49.1,140.1,634.8 +Sep '18,United Spirits Ltd,5448.55,2228.1,15.33,144.33,0.45,0.12,3.56,19.16,-94.7,140.2,513.8 +Jun '18,United Spirits Ltd,4879.75,2011.9,13.84,593.57,0.54,0.04,1.12,21.22,106.4,138.5,664.8 +Mar '18,United Spirits Ltd,4171.72,2173.7,14.96,43.05,0.44,0.06,14.52,22.17,-110.7,136.5,625.15 +Dec '17,United Spirits Ltd,3930.99,2263.3,15.57,79.2,0.48,0.07,9.27,21.29,5.9,137.2,734.23 +Sep '17,United Spirits Ltd,3413.19,1951.3,13.43,45.5,0.45,0.09,10.53,24.7,-45.7,135.2,479.11 +Jun '17,United Spirits Ltd,2975.01,1781.8,12.26,110.69,0.53,0.04,4.33,27.05,132.8,132,479.27 +Mar '17,United Spirits Ltd,3355.09,2025,13.94,-60.63,0.47,0.24,-7.17,17.39,63,130.9,434.69 +Dec '16,United Spirits Ltd,3572.84,2494.31,17.16,38.2,0.44,0.06,10.16,14.12,23.16,130.4,388.15 +Sep '16,United Spirits Ltd,3006.05,2048.3,14.09,86.72,0.4,0.04,5.68,17.19,-53.26,130.9,492.58 +Jun '16,United Spirits Ltd,3208.66,2040.54,14.04,171.32,0.44,0.04,2.9,17.26,11.5,130.1,496.84 +Mar '16,United Spirits Ltd,2503.95,2043.4,14.06,4999,0.44,0.03,0.1,19.58,73,126,499.9 +Dec '15,United Spirits Ltd,2435.4,2422.98,16.67,233.31,0.36,0.05,2.56,16.51,-93.59,126.1,597.28 +Sep '15,United Spirits Ltd,3064.43,5369.91,36.95,126.4,0.78,0.03,4.9,7.45,-88.31,125.4,619.34 +Jun '15,United Spirits Ltd,3915.3,1866.15,12.84,214.12,0.37,0.01,3.15,21.44,-86.97,123,674.47 +Mar '15,United Spirits Ltd,3299.23,2051.25,14.11,-5.91,0.5,0.83,-123.81,28.32,50.46,120.2,732.15 +Dec '14,United Spirits Ltd,3806.55,2318.23,15.95,102.66,0.38,0.03,5.42,25.06,-46.7,119.4,556.4 +Sep '14,United Spirits Ltd,5835.69,2178.58,14.99,-251.44,0.41,0.06,-1.91,26.66,27.97,120.1,480.25 +Jun '14,United Spirits Ltd,6471.05,1923.9,13.24,-125.39,0.39,0.02,-3.82,30.19,-52.25,116.7,478.99 +Mar '20,Heritage Foods,2392.98,643.08,27.72,-5.16,0.13,-0.33,-45.33,1.83,-25.13,148.6,233.9 +Dec '19,Heritage Foods,4509.77,661.6,28.52,115.92,0.17,0.02,3.14,1.78,-6.58,150.4,364 +Sep '19,Heritage Foods,4282.87,665.59,28.69,126.78,0.18,0.02,3.03,1.77,7.04,145.8,384.15 +Jun '19,Heritage Foods,4149.67,747.18,32.21,86.72,0.25,0.03,4.68,1.58,11.47,142.9,405.85 +Mar '19,Heritage Foods,4432.04,693.44,29.89,105.35,0.27,0.03,5.17,3.91,-14.57,140.4,544.65 +Dec '18,Heritage Foods,3822.07,610.04,26.29,125.02,0.2,0.03,4.22,4.44,-7.95,140.1,527.6 +Sep '18,Heritage Foods,5448.55,758.36,32.69,118.05,0.4,0.03,4.3,3.57,26.05,140.2,507.6 +Jun '18,Heritage Foods,4879.75,634.93,27.37,142.23,0.25,0.03,4.29,4.27,30.38,138.5,610.15 +Mar '18,Heritage Foods,4171.72,555.11,23.93,156,0.17,0.04,4.42,6.09,-25.78,136.5,689.5 +Dec '17,Heritage Foods,3930.99,571.52,24.63,254.95,0.18,0.03,3.24,5.91,-16.94,137.2,826.05 +Sep '17,Heritage Foods,3413.19,608.01,26.21,379.53,0.21,0.01,1.91,5.56,18.83,135.2,724.9 +Jun '17,Heritage Foods,2975.01,609.04,26.25,168.13,0.15,0.01,3.3,5.55,-19.16,132,554.83 +Mar '17,Heritage Foods,3355.09,330.8,14.26,85.16,0.18,0.04,6.32,7.82,-13.28,130.9,538.2 +Dec '16,Heritage Foods,3572.84,667.07,28.75,51.41,0.22,0.03,8.6,3.88,-3.84,130.4,442.13 +Sep '16,Heritage Foods,3006.05,641.31,27.64,63.35,0.25,0.02,6.77,4.03,20.84,130.9,428.9 +Jun '16,Heritage Foods,3208.66,634.23,27.34,36.35,0.25,0.03,7.12,4.08,19.92,130.1,258.8 +Mar '16,Heritage Foods,2503.95,632.71,27.27,32.84,0.2,0.03,7.78,1.97,-8.86,126,255.5 +Dec '15,Heritage Foods,2435.4,582.59,25.11,58.24,0.14,0.02,4.9,2.14,-40.83,126.1,285.38 +Sep '15,Heritage Foods,3064.43,586.83,25.29,29.56,0.25,0.03,6.59,2.12,20.62,125.4,194.8 +Jun '15,Heritage Foods,3915.3,578.45,24.93,40.53,0.2,0.02,4.62,2.15,0.98,123,187.25 +Mar '15,Heritage Foods,3299.23,544.02,23.45,30.33,0.17,0.02,5.43,1.59,-19.38,120.2,164.7 +Dec '14,Heritage Foods,3806.55,510.8,22.02,79.58,0.13,0.01,2.36,1.7,-26.56,119.4,187.8 +Sep '14,Heritage Foods,5835.69,512.38,22.09,83.04,0.17,0.01,2.08,1.69,-2.13,120.1,172.73 +Jun '14,Heritage Foods,6471.05,505.77,21.8,66.2,0.18,0.01,2.28,1.71,-2.72,116.7,150.93 +Mar '20,CCL Products India Ltd.,2392.98,170.24,6.4,29.26,0.49,0.57,7.25,16.05,-30.47,148.6,212.1 +Dec '19,CCL Products India Ltd.,4509.77,224.02,8.42,74.26,0.57,0.16,2.71,12.2,6.96,150.4,201.25 +Sep '19,CCL Products India Ltd.,4282.87,222.31,8.35,131.54,0.41,0.11,1.82,12.29,-9.03,145.8,239.4 +Jun '19,CCL Products India Ltd.,4149.67,206.08,7.74,41.4,0.49,0.4,6.18,13.26,3.28,142.9,255.85 +Mar '19,CCL Products India Ltd.,4432.04,195.78,7.36,76.68,0.52,0.25,3.71,20.98,16.06,140.4,284.5 +Dec '18,CCL Products India Ltd.,3822.07,180.96,6.8,150.98,0.44,0.13,1.79,22.69,-3.06,140.1,270.25 +Sep '18,CCL Products India Ltd.,5448.55,213.95,8.04,118.61,0.46,0.13,2.12,19.19,0.59,140.2,251.45 +Jun '18,CCL Products India Ltd.,4879.75,218.44,8.21,149.89,0.32,0.11,1.81,18.8,-13.56,138.5,271.3 +Mar '18,CCL Products India Ltd.,4171.72,224.8,8.45,158.15,0.37,0.1,1.76,17.81,0.59,136.5,278.35 +Dec '17,CCL Products India Ltd.,3930.99,203.62,7.65,141.85,0.4,0.14,2.11,19.66,-4.88,137.2,299.3 +Sep '17,CCL Products India Ltd.,3413.19,216.69,8.14,158.99,0.37,0.12,1.93,18.47,-6.61,135.2,306.85 +Jun '17,CCL Products India Ltd.,2975.01,178.22,6.7,188.87,0.37,0.12,1.55,22.46,-5.87,132,292.75 +Mar '17,CCL Products India Ltd.,3355.09,208.3,7.83,168.62,0.46,0.13,2.03,22.31,1.85,130.9,342.3 +Dec '16,CCL Products India Ltd.,3572.84,225.81,8.49,93.53,0.52,0.17,2.85,20.58,3.33,130.4,266.55 +Sep '16,CCL Products India Ltd.,3006.05,100.01,3.76,378.12,0.42,0.09,0.69,46.46,-1.42,130.9,260.9 +Jun '16,CCL Products India Ltd.,3208.66,173.26,6.51,133.55,0.39,0.14,1.86,26.82,-8.02,130.1,248.4 +Mar '16,CCL Products India Ltd.,2503.95,180.38,6.78,120.4,0.47,0.12,1.62,15,8.12,126,195.05 +Dec '15,CCL Products India Ltd.,2435.4,160.88,6.05,152.45,0.42,0.12,1.43,16.82,-0.66,126.1,218 +Sep '15,CCL Products India Ltd.,3064.43,178.09,6.69,147.7,0.37,0.12,1.59,15.19,-4.45,125.4,234.85 +Jun '15,CCL Products India Ltd.,3915.3,157.84,5.93,119.11,0.4,0.13,1.52,17.14,2.16,123,181.05 +Mar '15,CCL Products India Ltd.,3299.23,172.8,6.49,132.3,0.41,0.1,1.35,14.27,1.86,120.2,178.6 +Dec '14,CCL Products India Ltd.,3806.55,171.22,6.43,113.7,0.38,0.11,1.46,14.4,-3.64,119.4,166 +Sep '14,CCL Products India Ltd.,5835.69,195.35,7.34,70.9,0.39,0.11,1.61,12.62,5.01,120.1,114.15 +Jun '14,CCL Products India Ltd.,6471.05,132.6,4.98,58.56,0.37,0.12,1.18,18.6,-2.68,116.7,69.1 +Mar '20,Gillette India,2392.98,406.57,12.48,300.94,0.5,0.13,16.07,59.6,-23.37,148.6,4836.05 +Dec '19,Gillette India,4509.77,459.31,14.09,301.71,0.57,0.15,21.81,52.75,-13.76,150.4,6580.4 +Sep '19,Gillette India,4282.87,462.2,14.18,374.47,0.49,0.13,18.95,52.43,-22.52,145.8,7096.15 +Jun '19,Gillette India,4149.67,463.97,14.24,531.52,0.57,0.1,14.07,52.23,29.98,142.9,7478.55 +Mar '19,Gillette India,4432.04,465.51,14.28,243.77,0.63,0.19,26.93,44.58,22.27,140.4,6564.65 +Dec '18,Gillette India,3822.07,475.66,14.6,392.31,0.43,0.11,16.56,43.63,-76.93,140.1,6496.7 +Sep '18,Gillette India,5448.55,456.51,14.01,331.63,0.57,0.14,20.06,45.46,1.97,140.2,6652.5 +Jun '18,Gillette India,4879.75,409.76,12.57,606.49,0.62,0.08,10.62,50.65,15.8,138.5,6440.95 +Mar '18,Gillette India,4171.72,451.54,13.86,298.74,0.63,0.16,21.89,36.62,18.39,136.5,6539.35 +Dec '17,Gillette India,3930.99,407.52,12.5,376.04,0.55,0.14,18.03,40.57,-15.98,137.2,6779.95 +Sep '17,Gillette India,3413.19,408.03,12.52,283.46,0.6,0.16,19.75,40.52,2.65,135.2,5598.3 +Jun '17,Gillette India,2975.01,407.6,12.51,441.96,0.51,0.09,11.56,40.57,-20.19,132,5109.1 +Mar '17,Gillette India,3355.09,524.89,16.11,130.39,0.62,0.2,32.47,27.38,26.08,130.9,4233.75 +Dec '16,Gillette India,3572.84,389.38,11.95,255.14,0.49,0.14,16.8,36.91,-19.33,130.4,4286.3 +Sep '16,Gillette India,3006.05,411.73,12.63,257.05,0.5,0.13,16.83,34.91,-6.96,130.9,4326.15 +Jun '16,Gillette India,3208.66,509.69,15.64,317.69,0.51,0.09,14.4,28.2,-1.79,130.1,4574.7 +Mar '16,Gillette India,2503.95,452.78,13.89,177.44,0.48,0.12,24.88,32.46,-29.3,126,4414.65 +Dec '15,Gillette India,2435.4,508.38,15.6,292.35,0.56,0.1,15.95,28.91,-3.74,126.1,4663 +Sep '15,Gillette India,3064.43,481.73,14.78,473.67,0.56,0.07,10.22,30.51,-1.83,125.4,4840.9 +Jun '15,Gillette India,3915.3,540.91,16.6,204.27,0.64,0.13,22.35,27.17,33.82,123,4565.4 +Mar '15,Gillette India,3299.23,494.14,15.16,509.38,0.58,0.06,9.44,13.75,16.16,120.2,4808.55 +Dec '14,Gillette India,3806.55,498.47,15.3,290.33,0.53,0.07,11.31,13.63,-23.28,119.4,3283.6 +Sep '14,Gillette India,5835.69,439.98,13.5,511.7,0.51,0.04,5.43,15.44,-1.08,120.1,2778.55 +Jun '14,Gillette India,6471.05,479.72,14.72,415.75,0.57,0.03,5.14,14.16,15.92,116.7,2136.95 +Mar '20,Globus Spirits Ltd.,2392.98,269.54,9.36,12.56,0.35,0.08,7.48,1.34,-18.43,148.6,93.95 +Dec '19,Globus Spirits Ltd.,4509.77,352.91,12.25,27.79,0.44,0.04,5.1,1.02,13.26,150.4,141.75 +Sep '19,Globus Spirits Ltd.,4282.87,271.68,9.43,27.73,0.34,0.05,4.74,1.33,-19.17,145.8,131.45 +Jun '19,Globus Spirits Ltd.,4149.67,295.62,10.26,42.08,0.37,0.03,3.27,1.22,-1.4,142.9,137.6 +Mar '19,Globus Spirits Ltd.,4432.04,270.89,9.41,27.63,0.36,0.05,5.01,2.2,-4.03,140.4,138.45 +Dec '18,Globus Spirits Ltd.,3822.07,256.78,8.92,82.04,0.39,0.02,2.03,2.32,0,140.1,166.55 +Sep '18,Globus Spirits Ltd.,5448.55,225.14,7.82,149.29,0.42,0.01,0.98,2.65,4.64,140.2,146.3 +Jun '18,Globus Spirits Ltd.,4879.75,231.51,8.04,45.82,0.4,0.03,2.62,2.58,-3.62,138.5,120.05 +Mar '18,Globus Spirits Ltd.,4171.72,229.34,7.96,238.47,0.43,0.01,0.49,2.43,-6.5,136.5,116.85 +Dec '17,Globus Spirits Ltd.,3930.99,258.3,8.97,370.11,0.4,0,0.44,2.15,-4.42,137.2,162.85 +Sep '17,Globus Spirits Ltd.,3413.19,224.87,7.81,82.08,0.39,0.01,0.96,2.47,-9.97,135.2,78.8 +Jun '17,Globus Spirits Ltd.,2975.01,256.5,8.91,128.24,0.44,0.01,0.54,2.17,18.46,132,69.25 +Mar '17,Globus Spirits Ltd.,3355.09,210.67,7.31,381.25,0.39,0,0.2,2.21,-3.6,130.9,76.25 +Dec '16,Globus Spirits Ltd.,3572.84,220.32,7.65,122.78,0.39,0.01,0.72,2.12,-2.68,130.4,88.4 +Sep '16,Globus Spirits Ltd.,3006.05,161.86,5.62,11030,0.32,0,0.01,2.88,-12.44,130.9,110.3 +Jun '16,Globus Spirits Ltd.,3208.66,196.44,6.82,28.68,0.46,0.03,2.2,2.37,8.46,130.1,63.1 +Mar '16,Globus Spirits Ltd.,2503.95,190.98,6.63,43.53,0.42,0.02,1.5,2.05,-0.65,126,65.3 +Dec '15,Globus Spirits Ltd.,2435.4,196.66,6.83,64.21,0.43,0.02,1.21,1.99,2.42,126.1,77.7 +Sep '15,Globus Spirits Ltd.,3064.43,161.83,5.62,135.23,0.41,0.01,0.44,2.41,-3.12,125.4,59.5 +Jun '15,Globus Spirits Ltd.,3915.3,157.75,5.48,42.33,0.44,0.02,1.2,2.48,3.74,123,50.8 +Mar '15,Globus Spirits Ltd.,3299.23,152.66,5.3,32.48,0.38,0.02,1.31,1.57,-10.31,120.2,42.55 +Dec '14,Globus Spirits Ltd.,3806.55,155.56,5.4,1683.75,0.43,0,0.04,1.54,7.18,119.4,67.35 +Sep '14,Globus Spirits Ltd.,5835.69,136.49,4.74,813.5,0.35,0.01,0.1,1.75,-10.34,120.1,81.35 +Jun '14,Globus Spirits Ltd.,6471.05,140.96,6.13,169.61,0.43,0.02,0.51,1.7,14.15,116.7,86.5 +Mar '20,Hatsun Agro,2392.98,1266.38,78.32,786.68,0.31,0.01,0.5,7.22,39.88,148.6,393.34 +Dec '19,Hatsun Agro,4509.77,1340.18,82.88,253.45,0.27,0.02,1.74,6.82,-27.36,150.4,441 +Sep '19,Hatsun Agro,4282.87,1278.55,80.01,297.06,0.26,0.02,1.58,7.15,-51.87,145.8,469.35 +Jun '19,Hatsun Agro,4149.67,1423.22,89.06,169.6,0.38,0.04,3.17,6.43,112.68,142.9,537.64 +Mar '19,Hatsun Agro,4432.04,1183.04,74.03,422.79,0.33,0.02,1.25,10.11,27.27,140.4,528.49 +Dec '18,Hatsun Agro,3822.07,1154.48,72.25,419.96,0.26,0.02,1.12,10.36,-46.19,140.1,470.36 +Sep '18,Hatsun Agro,5448.55,1187.1,74.29,219.36,0.31,0.03,2.38,10.08,20.39,140.2,522.08 +Jun '18,Hatsun Agro,4879.75,1235.68,81.19,185.82,0.34,0.03,2.49,9.68,50.89,138.5,462.68 +Mar '18,Hatsun Agro,4171.72,1051.58,69.09,3202.44,0.27,0,0.16,10.95,-11.55,136.5,512.39 +Dec '17,Hatsun Agro,3930.99,1012.94,66.55,623.62,0.2,0.01,0.98,11.37,-87.56,137.2,611.15 +Sep '17,Hatsun Agro,3413.19,1066.84,70.09,204.82,0.23,0.04,2.51,10.8,-57.96,135.2,514.09 +Jun '17,Hatsun Agro,2975.01,1168.74,76.79,190.94,0.33,0.03,2.31,9.85,55.88,132,441.07 +Mar '17,Hatsun Agro,3355.09,1217.65,80,133.16,0.35,0.04,2.85,6.93,127.53,130.9,379.52 +Dec '16,Hatsun Agro,3572.84,946.82,62.21,141.97,0.18,0.03,1.89,8.92,-109.21,130.4,268.33 +Sep '16,Hatsun Agro,3006.05,1000.32,65.72,115.45,0.25,0.03,2.14,8.44,-32.22,130.9,247.06 +Jun '16,Hatsun Agro,3208.66,1034.28,95.15,87.78,0.33,0.03,2.67,8.16,65.53,130.1,234.38 +Mar '16,Hatsun Agro,2503.95,893.54,82.2,-140.67,0.34,-0.02,-1.54,5.54,65.89,126,216.63 +Dec '15,Hatsun Agro,2435.4,830.99,76.45,135.08,0.31,0.02,1.62,5.95,36.83,126.1,218.83 +Sep '15,Hatsun Agro,3064.43,852.95,78.47,73.43,0.22,0.04,2.86,5.8,-56.66,125.4,210.02 +Jun '15,Hatsun Agro,3915.3,867.11,79.77,70.16,0.19,0.03,2.64,5.71,-87.92,123,185.22 +Mar '15,Hatsun Agro,3299.23,771.41,70.97,207.95,0.22,0.01,0.8,5.02,-33.94,120.2,166.36 +Dec '14,Hatsun Agro,3806.55,713.23,65.61,137.57,0.14,0.02,1.19,5.43,-88.14,119.4,163.71 +Sep '14,Hatsun Agro,5835.69,745.2,69.19,178.56,0.23,0.01,0.95,5.2,-11.48,120.1,169.63 +Jun '14,Hatsun Agro,6471.05,703.25,65.3,216.43,0.21,0.01,0.68,5.51,-23.93,116.7,147.17 +Mar '20,Tasty Bite Eatables,2392.98,115.08,44.78,314.19,0.38,0.07,33.25,20.48,5.77,148.6,10446.95 +Dec '19,Tasty Bite Eatables,4509.77,114.17,44.42,155.82,0.33,0.12,53.31,20.64,-0.54,150.4,8306.8 +Sep '19,Tasty Bite Eatables,4282.87,105.67,41.12,226.27,0.31,0.1,42.71,22.3,-1.7,145.8,9664 +Jun '19,Tasty Bite Eatables,4149.67,90.85,35.35,312.34,0.27,0.08,29.96,25.94,-6.67,142.9,9357.85 +Mar '19,Tasty Bite Eatables,4432.04,85.43,33.24,266.96,0.38,0.09,31.31,25.39,-2.37,140.4,8358.6 +Dec '18,Tasty Bite Eatables,3822.07,87.76,34.15,332.6,0.38,0.08,27.01,24.71,0.94,140.1,8983.5 +Sep '18,Tasty Bite Eatables,5448.55,88.36,34.38,227.65,0.38,0.11,36.35,24.54,-1.44,140.2,8275.2 +Jun '18,Tasty Bite Eatables,4879.75,83.27,32.4,331.05,0.43,0.07,22.49,26.05,-1.11,138.5,7445.35 +Mar '18,Tasty Bite Eatables,4171.72,79.27,30.84,246.64,0.47,0.1,30.26,24.55,2.69,136.5,7463.2 +Dec '17,Tasty Bite Eatables,3930.99,72.81,28.33,300.39,0.38,0.09,26.32,26.73,-1.15,137.2,7906.2 +Sep '17,Tasty Bite Eatables,3413.19,85.05,33.09,162.94,0.41,0.1,32.84,22.88,-0.32,135.2,5351.05 +Jun '17,Tasty Bite Eatables,2975.01,59.19,23.03,350.04,0.38,0.06,13.69,32.88,-2.9,132,4792.1 +Mar '17,Tasty Bite Eatables,3355.09,70.18,27.31,147.28,0.44,0.12,31.63,17.8,-0.52,130.9,4658.5 +Dec '16,Tasty Bite Eatables,3572.84,64.67,25.16,180.39,0.45,0.08,19.14,19.32,2.38,130.4,3452.7 +Sep '16,Tasty Bite Eatables,3006.05,58.57,22.79,245.29,0.38,0.07,16.23,21.33,-0.41,130.9,3981.1 +Jun '16,Tasty Bite Eatables,3208.66,58.85,22.9,123.64,0.38,0.09,21.68,21.23,-2.02,130.1,2680.6 +Mar '16,Tasty Bite Eatables,2503.95,62.93,24.49,73.76,0.42,0.09,22.92,7.37,0.28,126,1690.5 +Dec '15,Tasty Bite Eatables,2435.4,51.85,20.18,121.74,0.4,0.07,13.35,8.94,0.5,126.1,1625.25 +Sep '15,Tasty Bite Eatables,3064.43,54.7,21.28,65.98,0.41,0.08,17.99,8.48,0.25,125.4,1187 +Jun '15,Tasty Bite Eatables,3915.3,38.84,15.11,146.45,0.34,0.06,8.53,11.94,-2.33,123,1249.25 +Mar '15,Tasty Bite Eatables,3299.23,45.55,17.72,48.95,0.45,0.07,12.36,4.13,1.28,120.2,605 +Dec '14,Tasty Bite Eatables,3806.55,40.44,15.74,65.36,0.38,0.06,8.92,4.65,-0.58,119.4,583 +Sep '14,Tasty Bite Eatables,5835.69,47.32,18.41,57.04,0.39,0.06,10.76,3.97,0.27,120.1,613.7 +Jun '14,Tasty Bite Eatables,6471.05,43.54,16.94,30.68,0.37,0.06,10,4.32,-1.06,116.7,306.8 +Mar '20,HU,2392.98,9011,41.72,293.01,0.51,0.18,7.02,54.53,-220,148.6,2056.9 +Dec '19,HU,4509.77,9808,45.41,257.46,0.56,0.18,7.47,50.1,136,150.4,1923.25 +Sep '19,HU,4282.87,9852,45.61,232.14,0.53,0.2,8.54,49.88,-191,145.8,1982.45 +Jun '19,HU,4149.67,10114,46.82,220.38,0.56,0.17,8.11,48.58,154,142.9,1787.3 +Mar '19,HU,4432.04,9945,46.04,240.2,0.54,0.17,7.11,36.72,140,140.4,1707.8 +Dec '18,HU,3822.07,9558,44.25,272.57,0.53,0.16,6.67,38.21,-118,140.1,1818.05 +Sep '18,HU,5448.55,9234,42.75,228.36,0.51,0.17,7.04,39.55,-90,140.2,1607.65 +Jun '18,HU,4879.75,9487,43.92,232.56,0.55,0.17,7.06,38.49,80,138.5,1641.85 +Mar '18,HU,4171.72,9097,42.12,214.09,0.52,0.16,6.24,31.35,-55,136.5,1335.9 +Dec '17,HU,3930.99,8590,39.77,223.18,0.54,0.16,6.13,33.2,-51,137.2,1368.1 +Sep '17,HU,3413.19,8309,38.47,199.18,0.53,0.14,5.9,34.32,-17,135.2,1175.15 +Jun '17,HU,2975.01,8529,39.49,182.39,0.53,0.15,5.93,33.44,52,132,1081.6 +Mar '17,HU,3355.09,8213,37.95,166.32,0.5,0.14,5.47,23.72,-62,130.9,909.75 +Dec '16,HU,3572.84,7705.98,35.6,172.15,0.53,0.09,4.8,25.28,149.45,130.4,826.3 +Sep '16,HU,3006.05,7842.69,36.24,171.5,0.51,0.14,5.06,24.84,119.95,130.9,867.8 +Jun '16,HU,3208.66,8127,37.63,165.79,0.51,0.13,5.42,23.97,-52,130.1,898.6 +Mar '16,HU,2503.95,7721,35.68,168.83,0.51,0.13,5.15,23.97,-52,126,869.5 +Dec '15,HU,2435.4,7763.95,35.88,192.06,0.53,0.15,4.49,23.83,76.7,126.1,862.35 +Sep '15,HU,3064.43,7731.39,35.73,182.76,0.5,0.13,4.45,23.94,20.68,125.4,813.3 +Jun '15,HU,3915.3,7844.47,36.25,185.52,0.51,0.13,4.94,23.59,41.97,123,916.45 +Mar '15,HU,3299.23,7675.63,35.48,185.33,0.5,0.09,4.71,24.27,-16.22,120.2,872.9 +Dec '14,HU,3806.55,7774.32,35.94,131.17,0.51,0.06,5.79,23.97,38.24,119.4,759.5 +Sep '14,HU,5835.69,7639.33,35.31,163.21,0.49,0.12,4.57,24.39,50.91,120.1,745.85 +Jun '14,HU,6471.05,7716.34,35.67,126.69,0.48,0.13,4.89,24.15,-14.65,116.7,619.5 +Mar '20,Emami,2392.98,460.45,10.16,-236.22,0.55,-0.08,-0.82,16.95,-43.36,148.6,193.7 +Dec '19,Emami,4509.77,748.28,16.49,95.03,0.73,0.21,3.26,10.43,40.79,150.4,309.8 +Sep '19,Emami,4282.87,592.12,13.05,150.81,0.65,0.16,2.11,13.18,-23.49,145.8,318.2 +Jun '19,Emami,4149.67,588.55,12.97,164.12,0.67,0.14,1.82,13.26,22.5,142.9,298.7 +Mar '19,Emami,4432.04,577.96,12.73,373.83,0.58,0.08,1.07,31.32,-8.98,140.4,400 +Dec '18,Emami,3822.07,758.51,16.71,137.57,0.71,0.21,3.04,23.86,38.33,140.1,418.2 +Sep '18,Emami,5448.55,571.92,12.6,285.17,0.62,0.14,1.73,31.65,-36.04,140.2,493.35 +Jun '18,Emami,4879.75,574.88,12.67,593.15,0.67,0.07,0.89,31.49,7.78,138.5,527.9 +Mar '18,Emami,4171.72,568.92,25.06,241.63,0.59,0.09,2.21,43.06,-24,136.5,534 +Dec '17,Emami,3930.99,715.59,31.52,99.88,0.7,0.21,6.57,34.23,11.95,137.2,656.2 +Sep '17,Emami,3413.19,579.01,25.51,119.5,0.71,0.18,4.58,42.31,24.82,135.2,547.3 +Jun '17,Emami,2975.01,499.1,21.99,1915.71,0.58,0.01,0.28,49.08,-23.25,132,536.4 +Mar '17,Emami,3355.09,527.68,23.25,174.65,0.58,0.13,3.02,41.15,-8.69,130.9,527.45 +Dec '16,Emami,3572.84,682.33,30.06,77.46,0.68,0.21,6.17,31.82,2.93,130.4,477.95 +Sep '16,Emami,3006.05,537.37,23.67,151.14,0.64,0.16,3.9,40.41,-13.74,130.9,589.43 +Jun '16,Emami,3208.66,593.37,26.14,253.47,0.64,0.08,2.17,36.59,6.31,130.1,550.03 +Mar '16,Emami,2503.95,539.11,23.75,150.61,0.58,0.13,3.09,41.74,-17.96,126,465.4 +Dec '15,Emami,2435.4,674.56,29.72,86.35,0.67,0.19,5.78,33.36,8.43,126.1,499.08 +Sep '15,Emami,3064.43,480.57,21.17,285.35,0.62,0.1,2.02,46.83,-10.61,125.4,576.4 +Jun '15,Emami,3915.3,494.32,21.78,154.65,0.6,0.17,3.75,45.52,0.05,123,579.93 +Mar '15,Emami,3299.23,495.74,21.84,86.23,0.64,0.27,5.83,19.42,13.14,120.2,502.73 +Dec '14,Emami,3806.55,626.76,27.61,55.08,0.67,0.26,7.16,15.36,9.19,119.4,394.38 +Sep '14,Emami,5835.69,444.1,19.56,88.73,0.59,0.2,3.93,21.68,-26.13,120.1,348.7 +Jun '14,Emami,6471.05,442.04,19.47,87.49,0.64,0.15,2.9,21.78,16.82,116.7,253.73 +Mar '20,Britannia,2392.98,2691.94,111.93,213.26,0.41,0.14,15.85,24.46,80.37,148.6,3380.2 +Dec '19,Britannia,4509.77,2819.19,117.22,201.95,0.4,0.13,14.99,23.36,-6.83,150.4,3027.26 +Sep '19,Britannia,4282.87,2896.09,120.52,143.72,0.38,0.15,20.5,22.74,-32.17,145.8,2946.2 +Jun '19,Britannia,4149.67,2579.46,107.34,263.55,0.41,0.11,10.41,25.53,20.14,142.9,2743.56 +Mar '19,Britannia,4432.04,2668.1,111.03,255.76,0.42,0.11,12.05,27.74,23.62,140.4,3081.96 +Dec '18,Britannia,3822.07,2703.19,112.49,247.49,0.38,0.11,12.6,27.38,-73.76,140.1,3118.4 +Sep '18,Britannia,5448.55,2704.62,112.55,122.85,0.4,0.1,23.61,27.37,13.73,140.2,2900.46 +Jun '18,Britannia,4879.75,2406.69,100.24,151.06,0.39,0.1,20.51,30.76,-12.61,138.5,3098.2 +Mar '18,Britannia,4171.72,2388.38,99.47,121.97,0.38,0.1,20.38,24.95,16.45,136.5,2485.74 +Dec '17,Britannia,3930.99,2411,100.42,113.63,0.34,0.1,20.75,24.72,-94.72,137.2,2357.88 +Sep '17,Britannia,3413.19,2385.38,99.35,106.32,0.4,0.1,20.43,24.98,60.56,135.2,2172.04 +Jun '17,Britannia,2975.01,2119.3,88.3,106.23,0.39,0.1,17.39,28.12,13.53,132,1847.34 +Mar '17,Britannia,3355.09,2089.34,87.06,103.2,0.37,0.09,16.39,19.4,-18.43,130.9,1691.46 +Dec '16,Britannia,3572.84,2115.96,88.17,82.07,0.38,0.1,17.56,19.16,-6.31,130.4,1441.1 +Sep '16,Britannia,3006.05,2222.39,92.6,88.74,0.37,0.1,18.97,18.24,-8.53,130.9,1683.4 +Jun '16,Britannia,3208.66,1984.91,82.7,78.63,0.39,0.11,17.53,20.42,-15.97,130.1,1378.4 +Mar '16,Britannia,2503.95,1960.39,81.68,83.66,0.42,0.1,15.99,16.37,43.19,126,1337.78 +Dec '15,Britannia,2435.4,1986.81,82.78,90.36,0.38,0.1,16.4,16.15,-36.73,126.1,1481.86 +Sep '15,Britannia,3064.43,1980.86,82.54,89.99,0.4,0.1,17.13,16.2,-9.17,125.4,1541.46 +Jun '15,Britannia,3915.3,1799.82,74.99,97.1,0.4,0.09,14.23,17.83,-4.41,123,1381.68 +Mar '15,Britannia,3299.23,1872.02,78.03,91.77,0.44,0.08,11.76,13.73,38.75,120.2,1079.26 +Dec '14,Britannia,3806.55,1852.33,77.21,91.55,0.38,0.08,10.05,13.88,-35.08,119.4,920.1 +Sep '14,Britannia,5835.69,1817.41,75.76,33.1,0.38,-0.04,21.1,14.14,-32.19,120.1,698.44 +Jun '14,Britannia,6471.05,1634.23,68.12,56,0.39,0.07,8.99,15.73,3.04,116.7,503.44 +Mar '20,Nestle,2392.98,3325.27,34.49,321.72,0.53,0.16,54.5,7.73,-100.13,148.6,17533.85 +Dec '19,Nestle,4509.77,3149.29,32.66,301.47,0.52,0.15,49.06,44.88,-164.91,150.4,14789.95 +Sep '19,Nestle,4282.87,3215.81,33.35,225.27,0.59,0.19,61.76,43.95,53.47,145.8,13912.5 +Jun '19,Nestle,4149.67,3000.85,31.12,262.51,0.58,0.15,45.41,47.1,-17.48,142.9,11920.65 +Mar '19,Nestle,4432.04,3002.95,31.14,228.81,0.58,0.15,48.05,47.07,-15.27,140.4,10994.25 +Dec '18,Nestle,3822.07,2897.27,30.05,332.06,0.56,0.12,33.45,36.42,-72.5,140.1,11107.25 +Sep '18,Nestle,5448.55,2939.36,30.48,209.76,0.61,0.15,46.27,35.9,37.26,140.2,9705.7 +Jun '18,Nestle,4879.75,2698.4,27.99,239.41,0.62,0.15,40.97,39.11,68.83,138.5,9808.55 +Mar '18,Nestle,4171.72,2757.24,28.6,186.5,0.58,0.15,43.98,38.27,-39.6,136.5,8202.15 +Dec '17,Nestle,3930.99,2601.46,26.98,242.58,0.56,0.12,32.34,28.53,-87.05,137.2,7845 +Sep '17,Nestle,3413.19,2514.05,26.07,202.93,0.58,0.14,35.59,29.52,30.31,135.2,7222.25 +Jun '17,Nestle,2975.01,2402.21,24.91,246.09,0.57,0.11,27.32,30.9,56.54,132,6723.05 +Mar '17,Nestle,3355.09,2491.88,25.84,209.97,0.53,0.12,31.82,29.78,-79.36,130.9,6681.2 +Dec '16,Nestle,3572.84,2286.16,23.71,347.34,0.55,0.18,17.36,25.06,-70.57,130.4,6029.8 +Sep '16,Nestle,3006.05,2363.49,24.51,230.07,0.61,0.14,27.94,24.24,52.7,130.9,6428.2 +Jun '16,Nestle,3208.66,2271.69,23.56,271.04,0.59,0.16,23.94,25.22,16.34,130.1,6488.75 +Mar '16,Nestle,2503.95,2283.72,23.69,193.59,0.56,0.11,29.8,25.09,-7.15,126,5768.95 +Dec '15,Nestle,2435.4,1959.46,20.32,306.53,0.54,0.15,19,28.41,-83.94,126.1,5824.1 +Sep '15,Nestle,3064.43,1742.36,18.07,493.04,0.61,0.12,12.88,31.95,61.68,125.4,6350.35 +Jun '15,Nestle,3915.3,1957.01,20.3,-949.95,0.62,0.45,-6.68,28.45,108.03,123,6345.65 +Mar '15,Nestle,3299.23,2516.48,26.1,208.97,0.55,0.13,33.22,22.12,-73.8,120.2,6941.9 +Dec '14,Nestle,3806.55,2530.94,26.25,188.47,0.55,0.12,33.85,24.14,-23.78,119.4,6379.8 +Sep '14,Nestle,5835.69,2570.42,26.66,185.24,0.57,0.13,32.28,23.77,71.4,120.1,5979.55 +Jun '14,Nestle,6471.05,2431.97,25.22,165.64,0.52,0.13,29.86,25.12,-5.43,116.7,4946 +Mar '20,Marico,2392.98,1188,9.21,196.63,0.39,0.19,1.75,29.87,-81,148.6,344.1 +Dec '19,Marico,4509.77,1434,11.12,163.52,0.49,0.19,2.09,24.75,44,150.4,341.75 +Sep '19,Marico,4282.87,1454,11.27,196.07,0.44,0.18,2.01,24.41,-22,145.8,394.1 +Jun '19,Marico,4149.67,1777,13.78,189.87,0.55,0.16,1.95,19.97,197,142.9,370.25 +Mar '19,Marico,4432.04,1290,9.99,103.68,0.26,0.33,3.33,34.36,-250,140.4,345.25 +Dec '18,Marico,3822.07,1499.81,11.62,183.55,0.47,0.18,2.04,29.56,68.41,140.1,374.45 +Sep '18,Marico,5448.55,1496.75,11.59,190.23,0.42,0.15,1.75,29.62,14.65,140.2,332.9 +Jun '18,Marico,4879.75,1684.61,13.05,198.62,0.43,0.13,1.67,26.31,66.55,138.5,331.7 +Mar '18,Marico,4171.72,1213.76,9.4,350.65,0.28,0.27,0.93,34.73,-197.41,136.5,326.1 +Dec '17,Marico,3930.99,1337.59,10.36,173.2,0.39,0.18,1.86,31.52,-66.8,137.2,322.15 +Sep '17,Marico,3413.19,1246.28,9.66,248.6,0.44,0.13,1.25,33.83,-1.14,135.2,310.75 +Jun '17,Marico,2975.01,1372.78,10.64,203.86,0.48,0.14,1.54,30.71,43.73,132,313.95 +Mar '17,Marico,3355.09,1103.46,8.55,231.77,0.35,0.15,1.27,34.45,-155.87,130.9,294.35 +Dec '16,Marico,3572.84,1142.98,8.86,126.33,0.46,0.23,2.06,33.26,-23.01,130.4,260.25 +Sep '16,Marico,3006.05,1160.5,8.99,197.99,0.46,0.15,1.39,32.76,-35.49,130.9,275.2 +Jun '16,Marico,3208.66,1454.39,11.27,144.45,0.61,0.16,1.83,26.14,166.93,130.1,264.35 +Mar '16,Marico,2503.95,1034.9,8.02,207.26,0.46,0.14,1.17,30.13,-60.43,126,242.5 +Dec '15,Marico,2435.4,1234.82,9.57,124.34,0.46,0.19,1.82,25.25,-20.84,126.1,226.3 +Sep '15,Marico,3064.43,1154.19,17.89,198.24,0.43,0.11,1.02,27.01,-20.64,125.4,202.2 +Jun '15,Marico,3915.3,1444.09,22.39,159.52,0.52,0.13,1.41,21.59,139.13,123,224.93 +Mar '15,Marico,3299.23,991.94,15.38,88.78,0.33,0.14,2.18,25.25,-127.51,120.2,193.53 +Dec '14,Marico,3806.55,1192.88,18.5,79.9,0.48,0.11,2.04,21,65.81,119.4,163 +Sep '14,Marico,5835.69,1144.17,17.74,77.93,0.31,0.11,1.99,21.89,-111.15,120.1,155.08 +Jun '14,Marico,6471.05,1352.22,20.97,54.37,0.46,0.11,2.24,18.52,77.98,116.7,121.78 +Mar '20,Colgate Palmolive,2392.98,1071.26,39.38,185.76,0.65,0.19,7.51,31.42,-1.34,148.6,1395.05 +Dec '19,Colgate Palmolive,4509.77,1147.17,42.18,199.52,0.62,0.17,7.32,29.34,-37.48,150.4,1460.5 +Sep '19,Colgate Palmolive,4282.87,1221.8,44.92,167.79,0.66,0.2,8.97,27.55,17.15,145.8,1505.05 +Jun '19,Colgate Palmolive,4149.67,1084.86,39.88,181.24,0.66,0.16,6.22,31.02,0.11,142.9,1127.3 +Mar '19,Colgate Palmolive,4432.04,1153.75,42.42,173.34,0.66,0.18,7.26,29.39,15.68,140.4,1258.45 +Dec '18,Colgate Palmolive,3822.07,1099.35,40.42,190.03,0.63,0.17,7.06,30.84,-24.21,140.1,1341.6 +Sep '18,Colgate Palmolive,5448.55,1168.03,42.94,150.28,0.64,0.17,7.22,29.03,-11.12,140.2,1085 +Jun '18,Colgate Palmolive,4879.75,1041.3,38.28,169.72,0.65,0.12,6.97,32.56,-4.83,138.5,1182.95 +Mar '18,Colgate Palmolive,4171.72,1091.66,40.13,152.41,0.67,0.19,6.94,25.93,15.44,136.5,1057.7 +Dec '17,Colgate Palmolive,3930.99,1033.32,37.99,174.74,0.65,0.17,6.27,27.4,2.4,137.2,1095.6 +Sep '17,Colgate Palmolive,3413.19,1084.88,39.89,162.08,0.64,0.16,6.53,26.1,7.07,135.2,1058.4 +Jun '17,Colgate Palmolive,2975.01,978.11,35.96,221.88,0.63,0.14,5.01,28.95,-8.23,132,1111.6 +Mar '17,Colgate Palmolive,3355.09,1037.52,38.14,190.52,0.65,0.14,5.24,25.89,19.7,130.9,998.35 +Dec '16,Colgate Palmolive,3572.84,874.58,32.15,192.68,0.63,0.15,4.7,30.71,-11.53,130.4,905.6 +Sep '16,Colgate Palmolive,3006.05,1056.58,38.84,145.56,0.63,0.17,6.67,25.42,-4.8,130.9,970.9 +Jun '16,Colgate Palmolive,3208.66,1013.14,37.25,198.85,0.62,0.12,4.62,26.51,2.21,130.1,918.7 +Mar '16,Colgate Palmolive,2503.95,1015.59,37.34,157.05,0.61,0.14,5.27,21.88,-5.67,126,827.65 +Dec '15,Colgate Palmolive,2435.4,956.84,35.18,159.86,0.63,0.17,6.07,23.23,-1.36,126.1,970.35 +Sep '15,Colgate Palmolive,3064.43,964.82,35.47,166.47,0.62,0.16,5.77,23.03,-4.95,125.4,960.55 +Jun '15,Colgate Palmolive,3915.3,930.94,68.45,238.78,0.62,0.19,4.26,23.87,7.73,123,1017.2 +Mar '15,Colgate Palmolive,3299.23,1028.51,75.63,83.68,0.64,0.16,12.03,26.38,-0.17,120.2,1006.73 +Dec '14,Colgate Palmolive,3806.55,996.01,73.24,92.74,0.63,0.13,9.62,27.24,-3.9,119.4,892.13 +Sep '14,Colgate Palmolive,5835.69,1000.52,73.57,91.27,0.62,0.13,9.53,27.11,-11.69,120.1,869.78 +Jun '14,Colgate Palmolive,6471.05,956.9,70.36,76.2,0.63,0.14,9.92,28.35,-0.59,116.7,755.9 +Mar '20,Godrej Consumer,2392.98,1113.94,10.9,269.74,0.51,0.22,2.35,47.99,-91.1,148.6,633.9 +Dec '19,Godrej Consumer,4509.77,1523.87,14.91,191.82,0.63,0.24,3.57,35.08,51.3,150.4,684.8 +Sep '19,Godrej Consumer,4282.87,1521.28,14.88,209.85,0.57,0.22,3.27,35.14,-12.06,145.8,686.2 +Jun '19,Godrej Consumer,4149.67,1315.36,12.87,282.36,0.57,0.18,2.35,40.64,-25.04,142.9,663.55 +Mar '19,Godrej Consumer,4432.04,1356.09,13.27,77.47,0.62,0.67,8.87,51.72,32.67,140.4,687.15 +Dec '18,Godrej Consumer,3822.07,1505.64,14.73,250.35,0.63,0.22,3.25,46.59,44.66,140.1,813.65 +Sep '18,Godrej Consumer,5448.55,1507.61,14.75,265.85,0.57,0.2,2.89,46.53,-35.42,140.2,768.3 +Jun '18,Godrej Consumer,4879.75,1309.97,19.23,252.04,0.57,0.17,3.24,53.55,-14.16,138.5,816.6 +Mar '18,Godrej Consumer,4171.72,1369.76,20.11,168.22,0.6,0.22,4.33,54.27,-16.85,136.5,728.4 +Dec '17,Godrej Consumer,3930.99,1425.04,20.92,151.75,0.59,0.21,4.39,52.17,-28.3,137.2,666.17 +Sep '17,Godrej Consumer,3413.19,1363.78,20.02,162.36,0.66,0.19,3.76,54.51,110.62,135.2,610.47 +Jun '17,Godrej Consumer,2975.01,1102.22,16.18,293.82,0.52,0.14,2.19,67.45,-19.23,132,643.47 +Mar '17,Godrej Consumer,3355.09,1254.94,36.84,74.92,0.58,0.2,7.43,45.37,0.72,130.9,556.67 +Dec '16,Godrej Consumer,3572.84,1195.91,35.11,74.22,0.54,0.19,6.79,47.6,-50.01,130.4,503.93 +Sep '16,Godrej Consumer,3006.05,1240.34,36.42,84.9,0.58,0.17,6.22,45.9,28.51,130.9,528.07 +Jun '16,Godrej Consumer,3208.66,1058.16,31.08,120.03,0.57,0.14,4.45,53.8,16.99,130.1,534.13 +Mar '16,Godrej Consumer,2503.95,1151.84,33.83,74.76,0.6,0.18,6.15,40.65,57.92,126,459.79 +Dec '15,Godrej Consumer,2435.4,1199.81,35.24,77.26,0.53,0.16,5.69,39.02,-52.19,126.1,439.59 +Sep '15,Godrej Consumer,3064.43,1231.55,36.17,75.05,0.58,0.15,5.4,38.02,-22.28,125.4,405.29 +Jun '15,Godrej Consumer,3915.3,1063.39,31.23,103.08,0.54,0.13,3.98,44.03,-31.69,123,410.27 +Mar '15,Godrej Consumer,3299.23,1148.36,33.74,62.85,0.59,0.16,5.52,30.42,22.2,120.2,346.93 +Dec '14,Godrej Consumer,3806.55,1183.9,34.78,64.03,0.56,0.15,5.05,29.51,7.8,119.4,323.37 +Sep '14,Godrej Consumer,5835.69,1106.01,32.49,64.39,0.54,0.16,5.1,31.59,13.12,120.1,328.39 +Jun '14,Godrej Consumer,6471.05,991.53,29.13,77.4,0.49,0.1,3.55,35.23,-38.07,116.7,274.77 +Mar '20,ITC,2392.98,10842.28,8.82,63.9,0.61,0.35,3.09,18.87,-174.43,148.6,197.45 +Dec '19,ITC,4509.77,12013.01,9.77,70.52,0.66,0.37,3.37,17.03,441.77,150.4,237.65 +Sep '19,ITC,4282.87,11871.47,9.66,79.18,0.65,0.34,3.28,17.23,330.98,145.8,259.7 +Jun '19,ITC,4149.67,11502.82,9.38,105.77,0.57,0.28,2.59,17.79,-774.66,142.9,273.95 +Mar '19,ITC,4432.04,11992.11,9.78,104.47,0.61,0.29,2.84,30.02,-51.02,140.4,296.7 +Dec '18,ITC,3822.07,11227.66,9.17,107.5,0.62,0.29,2.62,32.06,47.12,140.1,281.65 +Sep '18,ITC,5448.55,11068.85,9.04,122.6,0.62,0.27,2.42,32.52,21.68,140.2,296.7 +Jun '18,ITC,4879.75,10707.03,8.77,115.17,0.6,0.26,2.31,33.62,-197.92,138.5,266.05 +Mar '18,ITC,4171.72,10586.73,8.67,106.18,0.62,0.28,2.41,29.26,11.87,136.5,255.9 +Dec '17,ITC,3930.99,9772.02,8.02,103.58,0.65,0.23,2.54,31.7,140.22,137.2,263.1 +Sep '17,ITC,3413.19,9763.92,8.01,119.01,0.65,0.21,2.17,31.72,939.55,135.2,258.25 +Jun '17,ITC,2975.01,9954.66,8.19,153.48,0.61,0.26,2.11,31.11,-49.79,132,323.85 +Mar '17,ITC,3355.09,11125.54,9.16,127.48,0.63,0.24,2.2,30.38,514.23,130.9,280.45 +Dec '16,ITC,3572.84,9248.39,7.63,110.53,0.59,0.29,2.18,36.54,-400.9,130.4,240.95 +Sep '16,ITC,3006.05,9660.71,7.98,116.69,0.64,0.26,2.07,34.98,170.14,130.9,241.55 +Jun '16,ITC,3208.66,10054.04,12.48,124.56,0.61,0.24,1.97,33.61,360.7,130.1,245.38 +Mar '16,ITC,2503.95,9756.7,12.12,111.01,0.61,-1.69,1.97,26.39,-286.05,126,218.68 +Dec '15,ITC,2435.4,8867.06,11.03,104.99,0.61,0.28,2.08,29.03,-437.69,126.1,218.38 +Sep '15,ITC,3064.43,8798.89,10.96,116.62,0.64,0.26,1.88,29.26,-16,125.4,219.24 +Jun '15,ITC,3915.3,9160.02,11.43,116.67,0.65,0.24,1.8,28.11,543.19,123,210.01 +Mar '15,ITC,3299.23,9292.78,11.59,73.54,0.62,0.25,2.95,27.26,98.87,120.2,216.94 +Dec '14,ITC,3806.55,8942.59,11.18,74.42,0.63,0.29,3.3,28.33,167.01,119.4,245.58 +Sep '14,ITC,5835.69,9023.74,11.32,81.3,0.62,0.27,3.04,28.07,123.45,120.1,247.14 +Jun '14,ITC,6471.05,9248.29,11.63,78.76,0.5,0.24,2.75,27.39,-603.86,116.7,216.58 +Mar '20,Dabur India,2392.98,1321.15,7.48,321.79,0.44,0.22,1.45,59.91,-58.46,148.6,466.6 +Dec '19,Dabur India,4509.77,1748.18,9.89,245.13,0.53,0.21,1.87,45.28,71.81,150.4,458.4 +Sep '19,Dabur India,4282.87,1612.2,9.12,243.02,0.45,0.25,1.84,49.1,-64.87,145.8,447.15 +Jun '19,Dabur India,4149.67,1628.27,9.21,274.32,0.47,0.18,1.46,48.61,-18.37,142.9,400.5 +Mar '19,Dabur India,4432.04,1598.43,9.05,174.06,0.48,0.26,2.35,45.21,-17.78,140.4,409.05 +Dec '18,Dabur India,3822.07,1664.46,9.42,243.5,0.52,0.19,1.77,43.41,68.21,140.1,431 +Sep '18,Dabur India,5448.55,1537.2,8.7,245.75,0.45,0.2,1.74,47.01,-52.44,140.2,427.6 +Jun '18,Dabur India,4879.75,1473.1,8.34,298.4,0.48,0.16,1.31,49.05,12.1,138.5,390.9 +Mar '18,Dabur India,4171.72,1509.62,8.57,162.69,0.47,0.23,2.01,38.29,-33.27,136.5,327 +Dec '17,Dabur India,3930.99,1449.31,8.23,230.86,0.44,0.18,1.51,39.88,-75.9,137.2,348.6 +Sep '17,Dabur India,3413.19,1416.39,8.04,189.47,0.53,0.2,1.61,40.81,74.47,135.2,305.05 +Jun '17,Dabur India,2975.01,1233.74,7,304.32,0.42,0.16,0.96,46.85,-39.33,132,292.15 +Mar '17,Dabur India,3355.09,1434.8,8.15,161.13,0.46,0.21,1.72,34.21,1.62,130.9,277.15 +Dec '16,Dabur India,3572.84,1283.96,7.29,213.12,0.52,0.18,1.3,38.22,77.33,130.4,277.05 +Sep '16,Dabur India,3006.05,1351.58,7.67,177.32,0.45,0.2,1.53,36.31,-48.35,130.9,271.3 +Jun '16,Dabur India,3208.66,1278.86,7.26,272.74,0.45,0.16,1.13,38.38,-38.79,130.1,308.2 +Mar '16,Dabur India,2503.95,1432.97,8.15,152.85,0.49,0.2,1.63,30.61,21.21,126,249.15 +Dec '15,Dabur India,2435.4,1372.85,7.8,196.17,0.49,0.18,1.41,31.95,-2.18,126.1,276.6 +Sep '15,Dabur India,3064.43,1320.48,7.51,212.54,0.45,0.17,1.3,33.22,-24.39,125.4,276.3 +Jun '15,Dabur India,3915.3,1278.32,7.28,283.23,0.45,0.14,0.99,34.31,-18.41,123,280.4 +Mar '15,Dabur India,3299.23,1390.51,7.92,219.38,0.49,0.19,1.21,33.54,-2.33,120.2,265.45 +Dec '14,Dabur India,3806.55,1501.78,8.55,189.88,0.56,0.14,1.23,31.05,109.41,119.4,233.55 +Sep '14,Dabur India,5835.69,1293,7.36,207.66,0.42,0.15,1.07,36.06,-73.4,120.1,222.2 +Jun '14,Dabur India,6471.05,1245.99,7.09,228.35,0.41,0.12,0.82,37.43,-65.93,116.7,187.25 +Mar '20,Jubiliant Foodworks,2392.98,897.85,6.8,208.62,0.75,0.1,1.59,20.9,1.31,148.6,331.7 +Dec '19,Jubiliant Foodworks,4509.77,1059.6,8.03,42.05,0.75,0.1,7.86,17.71,-0.09,150.4,330.52 +Sep '19,Jubiliant Foodworks,4282.87,988.23,7.49,47.26,0.75,0.1,5.75,18.99,-1.68,145.8,271.77 +Jun '19,Jubiliant Foodworks,4149.67,940.09,7.12,43.49,0.75,0.08,5.67,19.96,-0.55,142.9,246.57 +Mar '19,Jubiliant Foodworks,4432.04,865.2,6.56,51.53,0.76,0.1,5.6,21.44,2.7,140.4,288.58 +Dec '18,Jubiliant Foodworks,3822.07,929.05,7.04,34.26,0.75,0.1,7.31,19.97,-0.85,140.1,250.44 +Sep '18,Jubiliant Foodworks,5448.55,881.36,6.68,41.59,0.75,0.09,5.89,21.05,0.5,140.2,244.94 +Jun '18,Jubiliant Foodworks,4879.75,855.06,6.48,49.02,0.74,0.09,5.66,21.7,-1.53,138.5,277.48 +Mar '18,Jubiliant Foodworks,4171.72,779.82,11.82,22.55,0.74,0.09,10.32,19.52,-1.43,136.5,232.67 +Dec '17,Jubiliant Foodworks,3930.99,795.17,12.05,17.6,0.75,0.08,10.01,19.15,1.9,137.2,176.21 +Sep '17,Jubiliant Foodworks,3413.19,726.64,11.01,18.76,0.74,0.07,7.4,20.95,-1.79,135.2,138.81 +Jun '17,Jubiliant Foodworks,2975.01,678.82,10.29,26.12,0.76,0.04,3.62,22.43,-0.14,132,94.55 +Mar '17,Jubiliant Foodworks,3355.09,612.78,9.29,108.45,0.77,0.05,1.02,11.85,0.68,130.9,110.62 +Dec '16,Jubiliant Foodworks,3572.84,658.84,9.99,28.21,0.75,0.03,3.03,11.02,-0.35,130.4,85.48 +Sep '16,Jubiliant Foodworks,3006.05,665.54,10.11,29.27,0.75,0.03,3.28,10.91,-1.03,130.9,96.01 +Jun '16,Jubiliant Foodworks,3208.66,608.92,9.25,39.35,0.77,0.03,2.89,11.93,0.21,130.1,113.73 +Mar '16,Jubiliant Foodworks,2503.95,618.05,9.39,30.04,0.77,0.05,4.24,13.51,1.83,126,127.35 +Dec '15,Jubiliant Foodworks,2435.4,633.94,9.64,33.2,0.77,0.05,4.47,13.17,-1.26,126.1,148.39 +Sep '15,Jubiliant Foodworks,3064.43,587.53,8.95,48.13,0.76,0.04,3.33,14.21,-0.31,125.4,160.28 +Jun '15,Jubiliant Foodworks,3915.3,570.69,8.69,44.09,0.76,0.05,4.21,14.63,-0.46,123,185.63 +Mar '15,Jubiliant Foodworks,3299.23,542.11,8.27,30.74,0.76,0.06,4.81,17.83,0.95,120.2,147.88 +Dec '14,Jubiliant Foodworks,3806.55,554.37,8.46,25.76,0.75,0.06,5.34,17.44,-0.58,119.4,137.57 +Sep '14,Jubiliant Foodworks,5835.69,501.16,7.65,27.78,0.75,0.06,4.43,19.29,-0.15,120.1,123.06 +Jun '14,Jubiliant Foodworks,6471.05,476.83,7.29,30.87,0.74,0.06,4.24,20.27,-0.99,116.7,130.9 diff --git a/Nifty/FMCG-Stock-Price-Prediction/MLSL1_FMCG_SharePricePrediction_Trial1.ipynb b/Nifty/FMCG-Stock-Price-Prediction/MLSL1_FMCG_SharePricePrediction_Trial1.ipynb new file mode 100644 index 00000000..04d63d10 --- /dev/null +++ b/Nifty/FMCG-Stock-Price-Prediction/MLSL1_FMCG_SharePricePrediction_Trial1.ipynb @@ -0,0 +1,774 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ZoeEcXqELJq3" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "id": "m1cARZHHM4nG", + "outputId": "faa5663e-cd18-46e8-df9b-8e6469500aa1" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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QuartersCompanyTotal Income From OperationsReturn on Equity RatioPrice-Equity RatioGross MarginProfit MarginEPSTime Interest Earned RatioEV/Net Operating RevenueChange in InventoryCPIInflationClosing Stock Price
0Mar '20Procter and Gamble656.0520.21146.390.640.1428.0752.3952.15-16.18148.60.0584109.05
1Dec '19Procter and Gamble859.2726.47103.920.650.1641.8882.2539.8121.20150.40.0744352.30
2Sep '19Procter and Gamble852.1426.25111.130.590.1642.16504.8340.15-8.62145.80.0404685.15
3Jun '19Procter and Gamble637.2919.63219.220.550.1018.7315.2753.68-25.40142.90.0324106.05
4Mar '19Procter and Gamble699.3421.54132.340.610.1327.76258.3745.3925.80140.40.0293673.65
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\n", + " " + ], + "text/plain": [ + " Quarters Company Total Income From Operations \\\n", + "0 Mar '20 Procter and Gamble 656.05 \n", + "1 Dec '19 Procter and Gamble 859.27 \n", + "2 Sep '19 Procter and Gamble 852.14 \n", + "3 Jun '19 Procter and Gamble 637.29 \n", + "4 Mar '19 Procter and Gamble 699.34 \n", + "\n", + " Return on Equity Ratio Price-Equity Ratio Gross Margin Profit Margin \\\n", + "0 20.21 146.39 0.64 0.14 \n", + "1 26.47 103.92 0.65 0.16 \n", + "2 26.25 111.13 0.59 0.16 \n", + "3 19.63 219.22 0.55 0.10 \n", + "4 21.54 132.34 0.61 0.13 \n", + "\n", + " EPS Time Interest Earned Ratio EV/Net Operating Revenue \\\n", + "0 28.07 52.39 52.15 \n", + "1 41.88 82.25 39.81 \n", + "2 42.16 504.83 40.15 \n", + "3 18.73 15.27 53.68 \n", + "4 27.76 258.37 45.39 \n", + "\n", + " Change in Inventory CPI Inflation Closing Stock Price \n", + "0 -16.18 148.6 0.058 4109.05 \n", + "1 21.20 150.4 0.074 4352.30 \n", + "2 -8.62 145.8 0.040 4685.15 \n", + "3 -25.40 142.9 0.032 4106.05 \n", + "4 25.80 140.4 0.029 3673.65 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('FMCG_20companies.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nHGufFzdM8db", + "outputId": "77c0ec65-0168-4db8-a8db-747d30eb3cbd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(480, 14)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zDxezWkcJRGL", + "outputId": "e480738a-2fda-44be-f01b-372f46cec046" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 400 entries, 0 to 399\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Quarters 400 non-null object \n", + " 1 Company 400 non-null object \n", + " 2 Return on Equity Ratio 400 non-null float64\n", + " 3 Price-Equity Ratio 400 non-null float64\n", + " 4 Gross Margin 400 non-null float64\n", + " 5 Profit Margin 400 non-null float64\n", + " 6 EPS 400 non-null float64\n", + " 7 Time Interest Earned Ratio 359 non-null float64\n", + " 8 EV/Net Operating Revenue 400 non-null float64\n", + " 9 Change in Inventory 400 non-null float64\n", + " 10 CPI 400 non-null float64\n", + " 11 Inflation 400 non-null float64\n", + " 12 Closing Stock Price 400 non-null float64\n", + " 13 Month 400 non-null object \n", + " 14 Year 400 non-null int64 \n", + "dtypes: float64(11), int64(1), object(3)\n", + "memory usage: 47.0+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uPu6nSoao8An" + }, + "source": [ + "## Train Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tjzLSTFmo_Cw", + "outputId": "00bcda7c-6f60-4a08-c495-d1c4ce02b802" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((400, 16), (80, 16))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test = df[df[\"Quarters\"].isin([\"Mar '20\",\"Dec '19\",\"Sep '19\",\"Jun '19\"])]\n", + "df_train = df[~df['Quarters'].isin(df_test['Quarters'])]\n", + "\n", + "df_train.shape, df_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cAoXwi-DKVFJ", + "outputId": "4b19ea07-1f52-4ee6-8d67-ef32a375e1ec" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((400, 15), (400,))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train = df_train[\"Closing Stock Price\"]\n", + "X_train = df_train.drop(\"Closing Stock Price\",axis=1)\n", + "X_train.shape, y_train.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QlfHPVZb0qzL" + }, + "source": [ + "## Preprocessing Function" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "P_JA7jiI0t3v" + }, + "outputs": [], + "source": [ + "def preprocessor(df):\n", + " df['Month'],df['Year'] = df['Quarters'].str.split().str\n", + " df['Year'] = df['Year'].str.replace(\"'\",\"\").astype(int)\n", + " print(\"Quarter column split in Month and Year\")\n", + " df.drop(\"Quarters\",axis=1,inplace=True)\n", + " print(\"Quarter column dropped from dataframe\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yypXcp0XMp5f" + }, + "source": [ + "## Split Categorical and Numerical variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G55VBQztMR1U", + "outputId": "1035e014-7ece-400b-c64a-81ada1304828" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Price-Equity Ratio',\n", + " 'Return on Equity Ratio',\n", + " 'CPI',\n", + " 'Time Interest Earned Ratio',\n", + " 'Closing Stock Price',\n", + " 'Change in Inventory',\n", + " 'Inflation',\n", + " 'Gross Margin',\n", + " 'EPS',\n", + " 'Profit Margin',\n", + " 'EV/Net Operating Revenue']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_cols = list(X_train.columns)\n", + "cat_cols = list(X_train.select_dtypes(\"object\").columns)\n", + "num_cols = list(set(all_cols)- set(cat_cols))\n", + "num_cols" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vYYcRNcSOA1m" + }, + "source": [ + "## One Hot Encoding for Categorical Variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LyNPyYUPM0eO", + "outputId": "c73b5115-59cd-42ea-c1d4-bf1a92a3201b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[array(['Britannia', 'CCL Products India Ltd.', 'Colgate Palmolive',\n", + " 'Dabur India', 'Emami', 'Gillette India', 'Globus Spirits Ltd.',\n", + " 'Godrej Consumer', 'HUL', 'Hatsun Agro', 'Heritage Foods', 'ITC',\n", + " 'Jubiliant Foodworks', 'Marico', 'Nestle', 'Procter and Gamble',\n", + " 'Tasty Bite Eatables', 'Tata Consumer Products',\n", + " 'United Breweries Ltd.', 'United Spirits Ltd'], dtype=object),\n", + " array(['Dec', 'Jun', 'Mar', 'Sep'], dtype=object)]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "ohe_encoder = OneHotEncoder(handle_unknown='ignore')\n", + "ohe_encoder.fit(X_train[cat_cols])\n", + "ohe_encoder.categories_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QNYdqrxSQgDo", + "outputId": "94931d7c-755b-4fa4-f6ec-561315bf65a9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Company_Britannia',\n", + " 'Company_CCL Products India Ltd.',\n", + " 'Company_Colgate Palmolive',\n", + " 'Company_Dabur India',\n", + " 'Company_Emami',\n", + " 'Company_Gillette India',\n", + " 'Company_Globus Spirits Ltd.',\n", + " 'Company_Godrej Consumer',\n", + " 'Company_HUL',\n", + " 'Company_Hatsun Agro',\n", + " 'Company_Heritage Foods',\n", + " 'Company_ITC',\n", + " 'Company_Jubiliant Foodworks',\n", + " 'Company_Marico',\n", + " 'Company_Nestle',\n", + " 'Company_Procter and Gamble',\n", + " 'Company_Tasty Bite Eatables',\n", + " 'Company_Tata Consumer Products',\n", + " 'Company_United Breweries Ltd.',\n", + " 'Company_United Spirits Ltd',\n", + " 'Month_Dec',\n", + " 'Month_Jun',\n", + " 'Month_Mar',\n", + " 'Month_Sep']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_cat_names = list(ohe_encoder.get_feature_names(cat_cols))\n", + "encoded_cat_names" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lkGCYRPAO9A5" + }, + "source": [ + "## Standard Scaling for Numerical Variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bREc_Jd1OY5h", + "outputId": "e85f39e2-c63e-4a19-a29c-13c1fa1755e3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardScaler()" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "sc = StandardScaler()\n", + "sc.fit(X_train[num_cols])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WkdkQOGwPEc8", + "outputId": "9ae6713e-bf8e-4d31-e281-411b60910181" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Price-Equity Ratio', 'Return on Equity Ratio', 'CPI',\n", + " 'Time Interest Earned Ratio', 'Closing Stock Price',\n", + " 'Change in Inventory', 'Inflation', 'Gross Margin', 'EPS',\n", + " 'Profit Margin', 'EV/Net Operating Revenue'], dtype=object)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sc.feature_names_in_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MmSodx71RtEg" + }, + "source": [ + "## Creating Column Transformer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OElmwMf-Rafs" + }, + "outputs": [], + "source": [ + "from sklearn.compose import ColumnTransformer\n", + "preprocessor = ColumnTransformer(transformers=[('',preprocessor,all_cols)\n", + " ('cat', ohe_encoder, cat_cols),\n", + " ('sc', sc, num_cols)],\n", + " remainder='passthrough')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GeT1wpndSjBK" + }, + "source": [ + "## Linear Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_xz67_3ESgcS" + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "linear_reg = LinearRegression()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zZy9F1geTMPJ" + }, + "source": [ + "## Creating Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K--rKSZcTHDN" + }, + "outputs": [], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "lreg_v1 = Pipeline(steps=[('preprocessor', preprocessor),\n", + " ('regressor', linear_reg)],\n", + " verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 322 + }, + "id": "UIv9d2X-TaNg", + "outputId": "1a96c714-e5fb-415b-8a25-7b4b6f75d28a" + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlreg_v1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 392\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m 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\u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_all_finite\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"allow-nan\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m 114\u001b[0m raise ValueError(\n\u001b[1;32m 115\u001b[0m msg_err.format(\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg_dtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmsg_dtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m )\n\u001b[1;32m 118\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')." + ] + } + ], + "source": [ + "lreg_v1.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xw9GM4a5Tcqc" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyMxp6vl32pDB20BEF1+LM5x", + "include_colab_link": true, + "mount_file_id": "1cnHoXL2fiaxkLWXAseUOTb_XOcZ6Npot", + "name": "MLSL1_FMCG_SharePricePrediction_Trial1.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Nifty/FMCG-Stock-Price-Prediction/Moneycontrol_FMCG_companies_data_scraping.ipynb b/Nifty/FMCG-Stock-Price-Prediction/Moneycontrol_FMCG_companies_data_scraping.ipynb new file mode 100644 index 00000000..269f2d07 --- /dev/null +++ b/Nifty/FMCG-Stock-Price-Prediction/Moneycontrol_FMCG_companies_data_scraping.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JRw-u9HsFrJq" + }, + "outputs": [], + "source": [ + "# Import required packages.\n", + "import time\n", + "import pandas as pd\n", + "import requests\n", + "from bs4 import BeautifulSoup\n", + "from tqdm.notebook import tqdm_notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2hvDsQ6iFrJ5" + }, + "outputs": [], + "source": [ + "seed_url = 'https://www.moneycontrol.com/financials/hindustanunilever/results/quarterly-results/'\n", + "table_view_start = 2\n", + "table_view_end = 8\n", + "company_performance_dict = {} # Create empty dictionary" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dmFPBNbyFrJ7" + }, + "source": [ + "Define a method 'process_row' that takes a row as an argument. It creates a key from first column and then for remaining columns it creates a list of values. Finally it adds key value pair to the global dictionary.\n", + "\n", + "It also checks for the existance of key, if it id present then it extend list of values otherwise it creates new key, value pair." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6nO91oR7FrKB" + }, + "outputs": [], + "source": [ + "def process_row(row):\n", + " columns = row.find_all('td')\n", + " key_column = columns[0].text\n", + " q1_column = columns[1].text\n", + " q2_column = columns[2].text\n", + " q3_column = columns[3].text\n", + " q4_column = columns[4].text\n", + " q5_column = columns[5].text\n", + " \n", + " value_list = []\n", + " value_list.append(q1_column)\n", + " value_list.append(q2_column)\n", + " value_list.append(q3_column)\n", + " value_list.append(q4_column)\n", + " value_list.append(q5_column)\n", + " if key_column in company_performance_dict.keys():\n", + " existing_list = company_performance_dict[key_column]\n", + " existing_list.extend(value_list)\n", + " else:\n", + " # Add key, value pair to dictionary.\n", + " company_performance_dict[key_column] = value_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ojhLDlBBFrKE" + }, + "source": [ + "Define a method 'write_dictionary_to_file' which takes a file name as a parameter and write content of the global dictionary into csv file on the mounted drive." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s3-8SopoFrKG", + "outputId": "996df785-2c4f-4efd-bb6f-97b155e4dbc9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "company_codes = ['CPI','BI','CC10','DI','E06','GI22','GS','GCP','HAP','HFI',\n", + " 'HU','ITC','JF04','M13','NI','PGH','TBE','TT','UB02','US']\n", + "len(company_codes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PD7PhnyrFrKK", + "outputId": "da54f1be-83e2-4bd2-d65c-cc36be0b30a5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['E06', 'GI22', 'GS']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "company_codes[4:7]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "referenced_widgets": [ + "d349779d6eb84d6098f1deb55327df90" + ] + }, + "id": "MqTPIvxbFrKN", + "outputId": "d237006b-1aee-45d8-8176-e03e4090709c" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d349779d6eb84d6098f1deb55327df90", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=20.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting data for company: CPI\n", + "Extracting data for company: BI\n", + "Extracting data for company: CC10\n", + "Extracting data for company: DI\n", + "Extracting data for company: E06\n", + "Extracting data for company: GI22\n", + "Extracting data for company: GS\n", + "Extracting data for company: GCP\n", + "Extracting data for company: HAP\n", + "Extracting data for company: HFI\n", + "Extracting data for company: HU\n", + "Extracting data for company: ITC\n", + "Extracting data for company: JF04\n", + "Extracting data for company: M13\n", + "Extracting data for company: NI\n", + "Extracting data for company: PGH\n", + "Extracting data for company: TBE\n", + "Extracting data for company: TT\n", + "Extracting data for company: UB02\n", + "Extracting data for company: US\n", + "\n" + ] + } + ], + "source": [ + "# Iterate through list of companies.\n", + "df = pd.DataFrame()\n", + "\n", + "for company_code in tqdm_notebook(company_codes):\n", + " print(\"Extracting data for company: \",company_code)\n", + " time.sleep(5)\n", + " company_performance_dict = {}\n", + " # Iterate through each page\n", + " \n", + " for table_view in range (table_view_start, table_view_end):\n", + " \n", + " # Customize URL to make it company specific\n", + " full_url = seed_url + company_code + \"/\" + str(table_view) + \"#\" + company_code\n", + "\n", + " # Make request to fetch content from full_url & store page content into local object\n", + " response = requests.get(full_url)\n", + " page = BeautifulSoup(response.content, \"html.parser\")\n", + "\n", + " # Meaningfull content we are interested in are available in table, hence we will find\n", + " # table from the page and work through it to scrape necessary data\n", + " table = page.find(\"table\", attrs={\"class\", \"mctable1\"})\n", + "\n", + " # Once we have table, we will select all rows within it\n", + " #table_body = table.find('tbody')\n", + " rows = table.find_all('tr')\n", + " for row in rows:\n", + " # Call function for each row\n", + " try:\n", + " process_row(row)\n", + " except Exception:\n", + " pass\n", + " \n", + " \n", + " company_df = pd.DataFrame.from_dict(company_performance_dict)\n", + " company_df.insert(1, \"Ticker\", company_code)\n", + " company_df.rename(columns={ company_df.columns[0]: \"Quarters\" }, inplace=True)\n", + " \n", + " df = pd.concat([df,company_df],axis=0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dGLy6OS5FrKT", + "outputId": "fd4d5a2a-4bf6-47a5-e92e-59d856e6bae2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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QuartersTickerNet Sales/Income from operationsOther Operating IncomeTotal Income From OperationsEXPENDITUREConsumption of Raw MaterialsPurchase of Traded GoodsIncrease/Decrease in StocksPower & Fuel...Share Holding (%)Promoters and Promoter Group Shareholdinga) Pledged/Encumbered- Number of shares (Crores)- Per. of shares (as a % of the total sh. of prom. and promoter group)- Per. of shares (as a % of the total Share Cap. of the company)b) Non-encumbered- Number of shares (Crores).- Per. of shares (as a % of the total sh. of prom. and promoter group).- Per. of shares (as a % of the total Share Cap. of the company).
0Mar '21CPI1,275.018.181,283.19324.3172.2818.26--...--------------
1Dec '20CPI1,224.217.721,231.93317.3164.82-9.60--...--------------
2Sep '20CPI1,277.667.821,285.48365.71103.83-59.91--...--------------
3Jun '20CPI1,033.607.021,040.62242.2582.6627.45--...--------------
4Mar '20CPI1,062.358.911,071.26321.1258.84-1.34--...--------------
\n", + "

5 rows × 51 columns

\n", + "
" + ], + "text/plain": [ + " Quarters Ticker Net Sales/Income from operations Other Operating Income \\\n", + "0 Mar '21 CPI 1,275.01 8.18 \n", + "1 Dec '20 CPI 1,224.21 7.72 \n", + "2 Sep '20 CPI 1,277.66 7.82 \n", + "3 Jun '20 CPI 1,033.60 7.02 \n", + "4 Mar '20 CPI 1,062.35 8.91 \n", + "\n", + " Total Income From Operations EXPENDITURE Consumption of Raw Materials \\\n", + "0 1,283.19 324.31 \n", + "1 1,231.93 317.31 \n", + "2 1,285.48 365.71 \n", + "3 1,040.62 242.25 \n", + "4 1,071.26 321.12 \n", + "\n", + " Purchase of Traded Goods Increase/Decrease in Stocks Power & Fuel ... \\\n", + "0 72.28 18.26 -- ... \n", + "1 64.82 -9.60 -- ... \n", + "2 103.83 -59.91 -- ... \n", + "3 82.66 27.45 -- ... \n", + "4 58.84 -1.34 -- ... \n", + "\n", + " Share Holding (%) Promoters and Promoter Group Shareholding \\\n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + " a) Pledged/Encumbered - Number of shares (Crores) \\\n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + " - Per. of shares (as a % of the total sh. of prom. and promoter group) \\\n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + " - Per. of shares (as a % of the total Share Cap. of the company) \\\n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + " b) Non-encumbered - Number of shares (Crores). \\\n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + " - Per. of shares (as a % of the total sh. of prom. and promoter group). \\\n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + " - Per. of shares (as a % of the total Share Cap. of the company). \n", + "0 -- \n", + "1 -- \n", + "2 -- \n", + "3 -- \n", + "4 -- \n", + "\n", + "[5 rows x 51 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HrePXdKFFrKW", + "outputId": "f253afcb-a292-41aa-b6d1-87d584f978d9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(600, 51)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NNy2JiyfFrKY", + "outputId": "e6ab729e-df30-4ff0-b18c-a2f12696f079" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "UB02 30\n", + "CC10 30\n", + "HFI 30\n", + "BI 30\n", + "TBE 30\n", + "GCP 30\n", + "GS 30\n", + "US 30\n", + "NI 30\n", + "E06 30\n", + "ITC 30\n", + "PGH 30\n", + "GI22 30\n", + "TT 30\n", + "HAP 30\n", + "HU 30\n", + "M13 30\n", + "DI 30\n", + "CPI 30\n", + "JF04 30\n", + "Name: Ticker, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Ticker.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IzLs26QOFrKZ", + "outputId": "5aa57b06-8f35-479d-fbf7-260f3bf206ee" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Jun '17 22\n", + "Jun '14 21\n", + "Jun '18 21\n", + "Jun '16 20\n", + "Mar '20 20\n", + "Sep '16 20\n", + "Dec '20 20\n", + "Mar '16 20\n", + "Jun '15 20\n", + "Mar '18 20\n", + "Dec '15 20\n", + "Sep '18 20\n", + "Sep '19 20\n", + "Sep '17 20\n", + "Mar '19 20\n", + "Dec '18 20\n", + "Dec '16 20\n", + "Dec '17 20\n", + "Jun '20 20\n", + "Sep '20 20\n", + "Mar '17 20\n", + "Jun '19 20\n", + "Dec '14 20\n", + "Sep '14 20\n", + "Dec '19 20\n", + "Mar '15 20\n", + "Mar '21 20\n", + "Sep '15 20\n", + "Mar '14 18\n", + "Dec '13 17\n", + "Sep '13 1\n", + "Name: Quarters, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Quarters.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "l9DQObISFrKb" + }, + "outputs": [], + "source": [ + "df.to_csv(r'G:\\ISB AMPBA\\15. Supervised Learning 1\\FMCG_combined_company_data.csv',index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7ha0gDKFFrKd" + }, + "source": [ + "This raw data captured will be cleaned and processed for use in the ML Algorithm \n", + "## END" + ] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}